Treatment program effect and nursing patient classification as determinants of psychiatric nurse staffing

The influence of treatment programs of specialized psychiatric units of the need for nurse staffing was the problem area explored in this study. The major research question asked whether the treatment program of an inpatient psychiatric unit accounted for more variance in the need for nurse staffing than was attributable to a nursing patient classification system which measures patients' need for nursing care. The study was an extension of a larger research project completed for the Veterans Administration (VA). Six test hospitals in the VA system were used as the sample. The six test hospitals contained 30 impatient units with a variety of treatment programs. A nursing patient classification instrument developed as an outcome of the VA study was used to reclassify all 898 original study patients according to their need for nursing care. The direct care hours given to each category of patient were obtained by worksampling in the original study. The direct care hours were used to analyze the effect of treatment program in hierarchical regression analysis. The treatment program accounted for approximately 1% of the variance in direct care give to patients. The nursing patient classification instrument together with shift accounted for 17% of the variance in direct care. This finding was consistent with the original VA study in that other variable thought to influence the need for nurse staffing, such as amount of socialization groups or chronicity of patients, contributed 1% less to the variance in direct care. The nursing patient classification instrument is a valid indicator of the amount of nursing care required by patients in a wide variety of VA hospitals. A valid nursing patient classification instrument is particularly useful in determining staffing patterns and budget. Other applications of the data produced by a nursing patient classification instrument are in the areas of quality assurance and costing of nursing care services. With the advent of prospective payment systems for hospitals, it is crucial to the nursings' professional and economic survival to unbundle the costs of patients for nursing care from the daily hospital room rate. The outcomes of the VA study and this study will add to the knowledge base of the profession in the area of nursing patient classification and support the use of new nursing patient classification system, the MESA Psychiatric Patient Classification System (MEPP).

TREATMENT PROGRAM EFFECT AND NURSING PATIENT CLASSIFICATION AS DETERMINANTS OF PSYCHIATRIC NURSE STAFFING by Dale Helen Frost Evans A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy College of Nursing The University of Utah December 1985 Copyright © Dale Helen Frost Evans 1985 All Rights Reserved THE UNIVERSITY OF UTAH GRADUATE SCHOOL SUPERVISORY COMMITTEE APPROVAL of a dissertation submitted by Dale Helen Frost Evans This dissertation has been read by each member of the following supervisory commiltee and by majority vote has been found to be satisfactory. Ju1 y 17. 1985 J u 1 y 1 7, 1 985 ~j@i~· Bonnie c. ~ Ju1 y 17, 1985 Verla B. Collins - July 1 7, 1985 J u 1 y 1 7, 1 985 THE L:~IVERSITY OF L'TAH GRADUATE SCHOOL FINAL RE.L~DING APPR()V AL To the Graduate Council of The l'niversitv of L'tah: I ha\'e read the dissertation of OaJ e He] en Frost Evans in its final form and have found that (1) its format. citations. and bibliographic style are consistent and acceptable; (2) its illustrative materials including figures, tables. and charts are in place: and (3) the final manuscript is satisfactory to the Supervis­ory Committee and is ready for submission to the Graduate School. Beth V. Cole (:haiq)('rs()!J. Stl\wnison (:ol11l11ill('(' Approved for tht.' ~t;:~j()r Department t3Li~ Linda K. Amos, F.A.A.N. Chairman / Dean Appro\'ed 1'01' IIH' (;radllall' Council v'.s,=- Pw ,;... ~ ~_ James L. Clayt Dean 01 'I he (~ladu"l(' St I\llul ABSTRACT The influence of treatment programs of specialized psychiatric units on the need for nurse staffing was the problem area explored in this study. The major research question asked whether the treatment program of an inpatient psychiatric unit accounted for more variance in the need for nurse staffing than was attributable to a nursing patient classification system which measures patients' need for nurs­ing care. The study was an extension of a larger research project com­pleted for the Veterans Administration (VA). Six test hospitals in the VA system were used as the sample. The six test hospitals con­tained 30 inpatient units with a variety of treatment programs. A nursing patient classification instrument developed as an outcome of the VA study was used to reclassify all 898 original study patients according to their need for nursing care. The direct care hours given to each category of patient were obtained by worksampling in the original study. The direct care hours were used to analyze the effect of treatment program in a hierarchical regression analysis. The treat­ment program accounted for approximately 1% of the variance in direct care given to patients. The nursing patient classification instrument together with shift accounted for 17% of the variance in direct care. This finding was consistent with the original VA study in that other variables thought to influence the need for nurse staffing, such as amount of socialization groups or chronicity of patients, contributed 1% or less to the variance in direct care. The nursing patient classification instrument is a valid indicator of the amount of nursing care required by patients in a wide variety of VA hospitals. A valid nursing patient classification instrument is particu­larly useful in determining staffing patterns and budget. Other applications of the data produced by a nursing patient classification instrument are in the areas of quality assurance and costing of nursing care services. With the advent of prospective payment systems for hospitals, it is crucial to the nursings' professional and economic survival to unbundle the costs to patients for nursing care from the daily hospital room rate. The outcomes of the VA study and this study will add to the knowledge base of the profession in the area of nursing patient classification and support the use of a new nursing patient classification system, the MESA Psychiatric Patient Classifi­cation System (MEPP). v This dissertation is dedicated to my friend and mentor Mrs. Minnie H. Walton, R.N., M.S. TABLE OF CONTENTS ABSTRACT . . • • ACKNOWLEDGMENTS Chapter I. INTRODUCTION Background of Study Problem Statement • • Review of Literature . Conceptual Framework . Definitions .. Study Hypothesis II. METHODOLOGY .•• Synopsis of Veterans Administration Study .•. Study To Determine Impact of Treatment Program On Nurse Staffing • • . • . • Data Analysis III. RESULTS . IV. DISCUSSION. Assumptions and Limitations of Study Implications for Nursing ••.•. Implications for Further Studies Appendices A. PATIENT CLASSIFICATION INSTRUMENTS, WORKSAMPLING CODES, AND UNIT QUESTIONNAIRE .••.. B. DESCRIPTIONS OF TEST HOSPITALS AND GENERAL PSYCHIATRIC UNITS . . . . . C. PATIENT CLASSIFICATION FORM REFERENCES . . . . . . . . . . . . . Page iv xiii 4 7 8 24 25 28 29 29 37 52 56 60 63 65 66 71 79 109 111 ACKNOWLEDGMENTS Grateful acknowledgment is given to the Veterans Administra­tion; Miss Marjorie Quandt, Director of Systems Development; and Mr. T. J. McCollister, Deputy Director, Management System Services for permission to use the data gathered during the study of psychiatric nursing staffing methodology and profile. The generous contributions of time and support given by MESA Corporation and in particular, Mr. David Merrill, Senior Vice President, were key factors in the completion of this study. Supervisory committee members gave generously of their time and knowledge and a special thank you is given to them. Especially appreciated was the freedom the committee gave me to chart my own course. The support and helpful suggestions of friends and classmates have been greatly valued. Special recognition is given to Minnie H. Walton, to whom this dissertation is dedicated, for her mentorship throughout the last 15 years and her unfailing encouragement throughout my doctoral study. CHAPTER I INTRODUCTION Nursing service administrators have one of the most challenging and exciting positions in today's health care system. Nursing service administrators are expected to be adequately prepared as skilled professionals to function administratively both as managers of the environment and its resources, as well as knowledgeable facilitators of the clinical practice of nursing in the provision of patient care. Most authorities in nursing service believe a clinical background is essential to strengthen the foundation and credibility of the manage­ment decisions that nursing service administrators make, especially when such decisions impact directly on patient care. Nursing administrators' responsibilities include development of the philosophy of the nursing department, the setting and implemen­tation of standards and goals, development of staffing methodologies, and implementation of organizational modes of nursing within the framework of the organization's stated mission and financial resources. Numerous forces impact the nursing service administration. Forces are generated through multiple organizational, community, and professional mandates. In complex health care organizations, particularly inpati­ent care settings, the demands on the nurse administrator or nurse executive are primarily administrative with the delegation of clinical decisions to nurse clinicians. There is general agreement in the 2 profession that a basis for administrative practice is a sound back­ground in the theories of administration and management. However, McClure (1984) cautions that, "There is no universal theory that will be useful and successful, and our jobs will not be simplified by the application of a single theory" (p. 18). The use of strategies based on contingency theory (Morse & Lorsch, 1970) is suggested as a way to cope with both the internal and external factors which influence nurs­ing administration. External forces are seen by McClure (1984) as becoming in­creasingly more influential to all who work in the health care field. liThe way in which we manage human resources more clearly affects and is affected by the environment than ever before and this interaction can be expected to escalate in the years ahead ll (p. 18). The influence of external forces is clearly demonstrated in the area of staffing. The development of staffing methodologies has taken on more importance in the world of nursing service administration as cost containment efforts in the health care industry have escalated during the last decade. With the passage of H.R. 1900 (P.L. 98-21) in August 1983, a system of prospective payment was established for the recipients of Medicare and Medicaid. The establishment of prospective payment, using diagnostic related groups (DRGs), revolutionized the health care industry by paying health care providers at rates set in advance of ~he care given and considered fixed for a period of time. Health care providers could no longer bill the Medicare program for their costs and thus became at risk for cost overruns (Shaffer, 1983). 3 Although psychiatric hospitals and psychiatric units in com­munity hospitals are currently exempt from the prospective payment system, the Health Care Financing Administration has a mandate from Congress to bring all currently exempt facilities under prospective payment. Nursing administrators in psychiatric facilities will be faced with the same challenges for cost containment that their col­leagues have been grappling with since the enactment of the prospec­tive payment legislation. Nursing service departments traditionally have controlled the largest percentage of the hospital budget and have long been vulnerable to forces seeking to reduce the nursing budget as a cost saving measure, while at the same time responding to the administrative and medical demands for the provision of quality nursing care. If psychiatric nursing service administrators are to cope suc­cessfully with the challenges of the years ahead, they need the best management strategies available, especially in the administration of scarce human resources. Competition for resources will be keen as health care facilities are forced to cope with increasing regulatory pressures. One of the most important aspects of nursing administration is an adequate staffing methodology. The perennial question has been how many nursing staff are needed to provide the level of patient care established as the standard for the institution. Nursing staff­ing problems are pressing and nearly universal. liThe history of nursing can be said to be, in large part, a history of attempts to respond to patient ca~e needs with an appropriate organization and allocation of nurse staffing resource" (Young, Giovanetti, Lewison, & Thomas, 1980, p. 1). 4 Staffing methodologies for psychiatric nursing have lagged behind other speciality areas. Schroder and Washington (1982) claim that "even in the '80s, there are psychiatric hospitals where, be­cause of the lack of patient/ratio justification, one nurse is employed for 250 patients, housed in two or three different buildingsll (p. 111). The lag in the development of staffing methodologies for psychiatric nursing is due in part to lack of reliable and valid nursing patient classification systems which are the basis for the majority of staffing methodologies in other speciality areas. The primary purpose of nursing patient classification systems is to assist with the determination and allocation of nursing personnel resources needed to respond to patients' requirements for nursing care through the categorization or grouping of patients according to their care needs (Giovannetti & Thiessen, 1983). Background of Study The lack of a valid nursing patient classification system on which to base a staffing methodology was of particular concern to the Veterans Administration (VA) as Congress had mandated the development of staffing guidelines for all nursing specialities in the VA system. The guidelines were to be in place and reported to Congress in early 1985. The VA had made several attempts to develop a valid nursing patient classification system internally to replace the system that had been in use for years. results of their efforts. 5 However, they were not satisfied with the Near the end of fiscal year 1984, the VA entered into a contract with Minority Enterprise Service Associates (MESA), a consulting firm, to develop a valid nursing patient classi­fication system for use in psychiatric inpatient units systemwide. This author served as the principal investigator and project technical director for the study titled Psychiatric Nursing Staffing Methodology and Profile. Hereafter, the study will be referred to as the original VA study or OVAs. A major outcome of the OVAs was the development of a new nursing patient classification system, the MESA Psychiatric Patient Classification System (MEPP). MEPP groups psychiatric patients into categories that reflect their need for nursing care. Workload figures based on the average number of patients in each category are currently being used to determine staffing patterns for the psychiatric nursing units in the VA's national system. During the course of the OVAs, many questions were raised regarding the adequacy of nursing patient classification instruments in predicting staffing needs on psychiatric inpatient units. The ques­tion of program effect was of particular concern to VA administrators and to this researcher. Does the placement of patients on treatment program units actually imply that participation will meet a patient's individual care needs? If the answer to this question is true, there would be little need for an additional nursing patient classification system as patients would be classified and placed on specific treatment units according to their care needs using a medical or treatment team 6 classification system. An additional concern revolves around the effect of the frequently scheduled daily living and therapeutic activities of the various types of treatment units on the number of nursing staff required. For instance, if a majority of patient activities occur off the unit during the day shift, the nursing staff­ing may be planned around escorts for those activities rather than individual patient care needs. The nursing patient classification system may not account for the staffing effect of the treatment program activities as the system focused on the individual patient needs. The lack of widely accepted nurse staffing systems for psychia­tric nursing would be explained by the fact that previous attempts have used a classification of patients according to their dependency on nursing tasks that cOIJld be identified and timed. That focus may be on the wrong unit of analysis. The treatment program of the nurs­ing unit may be a better unit of analysis in that it accounts for the planned interactions between staff and patients that mayor may not be related to an assessment of physical and psychological-emotional care requirements as measured by a nursing patient classification system. The argument can be made that treatment programs on specialized units consume a majority of both the patients' and nursing staffs' time during a day, and the nursing care given to patients is based not on individual care needs but on program directives. Five distinct types of specialized treatment program units were identified in the sample hospitals used for the OVAs. Treatment 7 program goals on all of the specialized program units were predeter­mined by the professional staff in relation to a number of criteria including the needs of the institution, the availability of resources, the types of diagnostic categories of patients, the age range of the patients, specific treatment approaches or modalities, and expected outcomes of care. On many of the test units sampled, it appeared that the proposed treatment program goals for units influenced the nursing staff activities and level of staffing required. A frequent observa­tion of the nursing staff on behavioral modification units sampled was that more staff was required due to the need for rewarding patients' appropriate behavior with tokens or with some other types of reward. The head nurse of one of the specialized treatment units gave the following description of the unit during a discussion concerning the need for increased nurse staffing: Most of our patients are hostile and aggressive or have a history of being hostile or aggressive. All have been diagnosed as suffering from chronic schizophrenia. . . • Because of their emotional levels, their ability to tolerate frustration is very low and they require a lot of staff to support them, just to ,keep functioning. (J. Smith, personal communication, November 28, 1984) Problem Statement The purpose of this study is to explore the influence of prescribed treatment programs of specialized psychiatric inpatient units on the need for nurse staffing. More specifically, does the treatment program account for more of the variability in the need for nursing staff than a nurse staffing methodology based on a nursing 8 patient classification system? Review of Literature Nurse Staffing Methodologies Three major publications will be cited to set a framework for the study of nurse staffing methodologies, particularly those based on nursing patient classification systems. Aydelotte (1973) published a comprehensive review of the liter­ature relating to nurse staffing methodologies. Young et al. (1980) built on the work of Aydelotte and others in their review of the literature of factors affecting nurse staffing in acute care hospitals. These publications and the review of patient classification systems in nursing by Giovannetti (1978) provide a wealth of information with regard to most aspects of nurse staffing methodologies. Aydelotte (1973) summarizes staffing models based on deter-mination of patient care needs: Conceptually speaking, nursing practice and its delivery evolve from the patient population that the staff serves. The type of nursing practice, the amount, and the time of its delivery are derived from the requirements of the patients. (p. 11) The American Nurses Association (1977) gives as the primary problem in staffing determinations the development and execution of a rational program of staffing: While the program must satisfy the needs of third­party payers and health care providers, above all the program must assure that the nursing services delivered are appropriate to consumer needs and that the nursing care provided is scientifically and technologically sound. (p. 2) 9 Young et ale (1980) noted that staffing methodologies have frequently been developed and implemented without regard for the larger context within which the nursing process occurs. They recom­mend "a conceptual framework that portrays nursing as an organiza­tional system responsive to patient demands but affected by a variety of controllable and uncontrollable factors, some of which may be neither identifiable or measurable" (p. 18). They adapted a conceptual framework originally proposed by Jelinek, Dennis, Schwarzmann, and Luskin (1976). The framework developed by Jelinek et ale viewed the nursing process as being com­posed of four key components: input, technology, output, and environment. Young et ale (1980) modified this model in order to provide a framework for delineating variables and parameters asso­ciated with the organization and delivery of nursing care. In their framework, patient factors were viewed as inputs to the nursing care process and to nurse staffing methodologies. Patient factors include care requirements as reflected by nursing patient classification systems. The effects of patient classification systems on staffing methodologies were reviewed and a conclusion drawn that there was growing acceptance of the concepts of nursing patient classification systems and they were being widely implemented. Currently, staffing models using nursing patient classifi­cation are the most widely used and accepted. Additional benefits of nursing patient classification to nursing practice are outlined by Giovannetti and Thiessen (1983): 1. Baseline and variable staffing patterns can be determined. 2. Equitable distribution of nursing assignments can be achieved. 3. Workload trends can be monitored over days, weeks, or longer and can be used for long-range planning. 10 4. Care requirements can aid in the determination of staff­ing patterns. 5. Changes in nursing and/or medical practice can be moni­tored. 6. During periods of acute staff shortages, priority setting can be facilitated. 7. An evacuation or crisis plan can be developed uSing patient classification information. 8. Patient classification may be used as a foundation for developing process criteria, a component of a quality assurance program. 9. Patient classification information can also form the basis for determining costs of nursing care. Nursing Patient Classification Systems Giovannetti (1978) provides a comprehensive review of patient classification systems in nursing. Historically, development of patient classification systems can be traced over the last four decades. Giovannetti describes initial attempts at classifying patients according to intensities of nursing care requirements that occurred when the National League of Nursing Education published a 11 four-category patient classification system for pediatrics in 1947. The classification system rated patients by means of a 3-point scale of intensity on four factors. During the 1950s, the Army developed a method of patient classification based on four prototype categories. The Army prototype evaluation classified patients by descriptors or critical indicators of the "typical patientll in each category. These early systems provided models of the most frequent types of patient classification evaluation, factor evaluations, and prototype evalua-tions (Abdellah & Levine, 1965). Quantification, or an estimate of the nursing time requirements of patients, was a relatively new focus for patient classification systems when the investigations of time requirements took place at the John Hopkins Hospital in the early 1960s. Conner (1961) employed worksampling methods from industrial engineering to associate the amount of time spent with the patient in the performance of direct care with each class or category of patient based on a patient classifica­tion system. Further studies were then conducted to determine the relationship between direct care and the other types of nursing activities which made up the total workload (Young, 1962; Wolfe & Young, 1965). The significant findings from the studies at John Hopkins are cited by Giovannetti (1978): First was the relationship between workload and census. The investigators were able to demonstrate that patient care was not a function of gross census alone, but rather of the number of patients in each category of care present on the ward. Second, the variation in demand for nursing staff was found to be large relative to the average demand. Third, the variation in demand for nursing staff was independent from ward to ward. Fourth, the main determinant of nursing workload was the number of class III or intensive care patients. (p. 27) 12 The findings cited above provided the basis for further studies which concentrated on the concept of workload as determined by the number of patients in each category on an average day. Demand for nursing staff determined by workload factors rather than by average daily census alone provided greater sensitivity and accuracy in determining adequate nurse staffing patterns based on patients' need for nursing care. In the late 1960s, a proliferation of patient classification studies began. The California Commission for Administrative Services in Hospitals (CASH) was one of the first multihospital systems to study and develop standard times for nursing activities (Commission for Administrative Services in Hospitals, 1965). The use of standard times for nursing activities remains a problematic area for many nursing service administrators who are concerned about the reduction of nursing activities into those which can be easily observed and measured. However, the time measurement of common nursing activities did provide a standard for judging a nursing unit's efficiency. As increasing numbers of hospitals and management firms began to use the concepts of patient classification, the concepts gained wider acceptance. The decade of the 1970s brought an increased impetus for the widespread implementation of nursing patient classi­fication systems. The impteus came from the Joint Commission on Accreditation of Hospitals (JCAH) (1982). The interpretation of JCAH's Nursing Services Standard III states that I'the nursing 13 department/service shall define, implement, and maintain a system for determining patient requirements for nursing care on the basis of demonstrated patient needs, appropriate nursing intervention, and priority of care" (pp. 117-118). Alward (1983) estimates that 5,000 of the 7,000 short-term hospitals accredited by JACH can be presumed to be classifying pa­tients in order to staff according to patients' requirements. Many classification systems have been developed ad hoc by individual hos­pitals in response to the JCAH requirement. Other systems have been marketed commercially, i.e., Nursing Productivity and Quality System (NPAQ) by Medicus Systems, Incorporated. In 1977, the American Nurses' Association listed the basic assumptions of patient classification systems based on management engineering methodology: 1. Patients can be classified into groups and care needed for the group assigned. 2. It is valid to look at an "average patient" or lIaverage amount ll of care. 3. Nursing is a set of procedures that can be seen as separate and with starting and end[ingJ points. 4. The nursing unit selected for study should be repre­sentative of the quality desired, and the facts obtained in the study of one unit can be generalized to other similar nursing units. Data are generalizable from one setting and patient group to another. 5. Nursing care requirements can be quantified. (pp. 5-6) Some of the assumptions seem quite naive nearly a decade later. The fourth assumption has been questioned by most experts in the field of nurse staffing and nursing patient classification. In fact, one of the most frequently cited limitations of nursing patient classification systems is that they are not generalizable from one 14 setting to another. However, the assumptions reflect the professional organization's belief in the use of patient classification in the development of nurse staffing methodologies. Psychiatric Nursing Patient Classification Systems Developments in psychiatric nursing patient classification systems cited by Giovannetti (1978) include the following: 1. A l2-category classification instrument was developed at St. Elizabeth's Hospital, Washington, D.C. in the early 1960s. The instrument used prototype categories and reflected progres­sive care concepts which were concerned with the placement of patients during the course of their illness and recovery. Patients at St. Elizabeth's who were not eligible for discharge or for rehabilitation programs were categorized using the classifica­tion instrument and placed into the various units or sections of St. Elizabeth's Hospital. The classification system was specific to the unique set of problems at St. Elizabeth and was, there­fore, not applicable to other psychiatric settings. 2. A 4-category prototype classification was developed by the VA and modeled after the early Brooks Army Hospital System. The VA system outlined specific criteria describing patients requiring different levels of care. Examples of patients were provided for each level. The VA was still using a modified ver­sion of this classification system prior to the OVAs and were using the resulting data for the patient care reports required by the system. Staffing patterns were determined by the individual 15 hospitals in the system based on the numbers of available posi­tions allocated by the VA central administration. The VA central nursing service argued that the classification system was too subjective to be used to determine actual staffing patterns or to provide a sound data base for budget requests. 3. The California Department of Mental Hygiene's system, known as SCOPE (Staffing the Care of Patients Effectively), classified patients according to 12-13 "patient characteristics" depending on their diagnosis of mental illness or mental retarda­tion. If the patients were diagnosed as mentally retarded, their IQ level was noted as a patient characteristic. An example of a general patient characteristic was "feeding." Three levels were used to describe this characteristic: self, with assistance, and complete. Standard times for each characteristic were developed using direct time studies and work sampling. Reliability data were not reported in the original study. Young et al. (1980) reviewed another state system, SPAN (Staffing for Patients' Actual Needs), which is used by the Indiana State Department of Mental Health. This system is also based on patient charac~eristics and standard times for common nursing activities. Both the California and Indiana systems appear to be specific to the respective systems and have not been reported in use elsewhere. 4. In 1973, the Saskatchewan Hospital Systems Study Group carried out studies to develop a classification instrument unique to the total care requirements of short-term psychiatric patients. 16 A basic premise of the studies was that lithe delivery of patient care should be planned on the basis of an analysis of patient's individual care requirements, and not on the basis of routine or traditional practices" (Giovannetti & McKague, 1977, p. ii). A 4-category factor evaluation instrument was developed which placed patients in categories of care ranging from "minimal ll to "intensive care," based on care needs. During the course of the study, several factors were identified as being unique to psychiatric settings, including the mobility of the patient, the "therapeutic freedom II of the nurse in determining the amount of direct care time spent with patients, and the number of group activities in the direct care of patients. These factors were cited as possible explanations for the lack of predictive validity of the instrument in some of the test settings. A review of the current literature, 1976 to 1985, elicited seven articles describing nursing patient classification systems for psychiatry (Auger & Dee, 1983; Ganti, Nagy, & Johnson, 1976; Krupinski & Mundy, 1982; Manson, 1982; Schroder & Washington, 1982; Sherrod, 1984; Sovie, Tarcinale, Van Putee, & Stunden, 1985). Schroder and Washington (1982) identify problems that offer some explanation as to why nursing patient classification systems have not worked well in psychiatry: 1. Psychiatric nursing is harder to quantify than medical/ surgical nursing. 2. It is more difficult to put into a measurable time frame. 17 3. Unpredictability is considered to be greater in psychia­tric care than in other specialities. 4. Conceptualization of the processes and requirements of psychiatric nursing are not well defined by administration or by financial planners. In addition to these problems, the resistance of psychiatric nursing to the basic assumption of nursing patient classification systems is cited as a major obstacle. Psychiatric nurses tend to resist attempts to "average out" and/or "label" patients. The nursing patient classification system described by the authors was developed specifically for the Menninger Memorial Hospital in Topeka, Kansas. Patients were classified according to two major groups of nursing care needs, "routine" and lIextra. 1I "Routine" care indicators "included "supervision," "meals," "hygiene," "activities," "privilege level," "risk," IImedications," and "physical problems." "Extra" care was described in two categories, behavioral and extra demands. The 11 behavioral indicators included such descriptors as "elopement risk,1I "withdrawn ll "assaultive," and "cold wet sheet packs." "Extra" demands were described by six items, such as "family conference," lIadmission," and "discharge." Each descriptor for the activities/characteristics was weighted and the patient was placed into one of five categories by the value of the total scores on the descriptors. The categories or "level of acuityll ranged from "minimal" to "critical." The hours of care for each level of acuity were based on estimates of skilled clinicians and when validated by our own observations were found to be accurate enough for this purpose. The hours of care developed ranged from 1.5 hours to 7.0 hours. 18 Ratings of staffing adequacy were obtained from nursing supervisors periodically but the methodology was not considered as useful as anticipated. Interrater reliability and validity data are not reported. The method for determining direct and indirect care times is vague. care activities. An arbitrary 19% factor is used to account for non­The usefulness of the system is difficult to deter-mine due to these limitations. It is interesting to note that most of the indicators used to describe routine care were included in the four remaining nursing patient classification instruments tested in the OVAs. Auger and Dee (1983) describe the development of a nursing patient classification system based on the Johnson Behavioral System Model of Nursing at the UCLA-Neuropsychiatric Institute in Los Angeles, California. The methodology was developed by a committee of nursing administrators and clinicians. Each of Johnson's eight behavioral subsystems was operationalized in terms of critical "adaptive and maladaptive behaviors. 1I An expert panel of clinicians evaluated each behavioral statement for compliance with four criteria: measurable, relevant to the clinical setting, observable, and specific to the subsystem. The behaviors and nursing interventions were ranked in three cate­gories or levels. For example, "Level I" referred to adaptive behaviors requiring minimal nursing care. A fourth level was added later to account for maladaptive behaviors that are of such intensity 19 and frequency that continuous one-to-one intensive nursing care is required to ensure the safety of the patient and/or others in the environment. Estimates of the nursing care hours for each category were based on "typica111 patient care programs. Observational studies were planned to validate the estimates. The authors state that the system could be applied in all practice settings through the development of specific patient criteria. The system is quite complex and lengthy and may be more appropriate to university teaching hospitals who wish to use the classification system for clinical and teaching purposes, as well as administrative use, than to short-term general hospital units or to a large multi­hospital system, such as the VA. Auger and Dee (1983) highlight another obstacle in developing nursing patient classification systems for psychiatry: In the psychiatric setting, any time a new person [nurse] assesses the behavior of a patient, uncontrolled variables associated with that individual are introduced and may signifi­cantly affect the reported category of care of the patient. Such difficulties are minimized in systems that are based on specific nursing procedures and medical orders. (pp. 40-41) Ganti et ale (1976) developed a nursing patient classification system using five clusters of patient care activities: physical needs, safety and precautions, tests and treatments, behavior, and family interaction. Fifty-six indicators of care were placed under each of the clusters. These indicators were weighted by using a frequency chart with 5-point intervals. Staff classified patients by referring to each of the clusters and circling the appropriate point values of the indicators. The system appears to be a time-consuming 20 and complex one that has apparently not been used by other psychiatric nursing facilities. To determine the requirements for nurse staffing in acute psychiatric wards in Australia, Krupinski and Mundy (1982) developed a two-dimensional patient classification scheme that categorized patients in terms of their physical and psychological dependencies. "Descriptive criteria for a 16-cell, two-dimensional [four by four] patient dependency matrix were agreed upon by senior nursing staffll (p. 49). Nursing hours for each category of patient were then recom­mended based on a series of meetings with the nursing staff. No reliability or validity data are presented and the methodology for determining care hours is a major limitation of the study. Manson (1982) gives a very brief report of a nursing patient classification system developed at Westbrook Hospital in Virginia. The indicators of care were: activities of daily living, mental status, psychiatric nursing needs, and patient care needs. Five categories of classification, ranging from "ambulatory" to "acute" care, were developed using five sets of descriptors for each indicator. For example, one of the descriptors for psychiatric nursing needs was the privilege level of the patient, ranging from "full privileges with no escort" to "crisis intervention with verbal and/or physical restraints. II How the classification system is implemented is not spelled out. No information is given as to the quantification of the nursing hours for each category. One of the most promising nursing patient classification systems is briefly outlined by Sherrod (1984). The nursing patient 21 classification system was developed through 4 years of field research and consists of six subsystems: (a) critical care, (b) medical/ surgical, (c) obstetric, (d) psychiatric, (e) neonatal, and (f) pediatrics. There are five components in each of the subsystems: (a) a patient classification instrument, (b) the mathematical model, (c) instructional information, (d) tabulation form, and (e) methodo­logy for determining care provider mix. No detail is given on the psychiatric subsystem other than it has five categories with hours of care ranging from 1 through 24 hours and is a factor-evaluation instrument. Extensive validity and reliability studies were conducted and demonstrated that the system is reliable and valid. Validity mea­sures were content-related and criterion-related and had correlations (L) ranging from .87 to .99. The study conducted at the University of Rochester's Strong Memorial Hospital and reported by Sovie et ale (1985) used the Rush Medical Center Psychiatric instrument from which the Medicus NPAQ system was developed. The Strong Memorial Hospital has used the Rush classification system since 1977. A description of the Medicus system can be found in Appendix A, as it was one of the systems used in the OVAs. During the study period, only three levels of nursing acuity were used because a fourth category (minimal) of care patients was treated in ambulatory units. A total of 25,999 psychiatric patient days was included in the study and 65 different diagnoses were represented. Although the Rush-Medicus systenl is in use in a number of 22 hospitals, the outcome of the OVAs demonstrated that the new nursing patient classification system developed during the OVAs was more predictive than the Rush-Medicus system. Psychiatric Treatment Program and Nurse Staffing A search of the literature from 1965 to 1985 produced little published about the relationship between treatment programs and nurse staffing. Gunderson (1978), a psychiatrist, attempted an analysis of milieu treatment programs according to what he terms "functional variables." For each of the variables, i.e., containment, support, and validation, he describes the preferred personality characteris­tics and behaviors of the nursing staff. It is Gunderson's contention that the nursing staff must be carefully selected and the effect of their behaviors on patient behaviors is key to the success or failure of milieu treatment programs. Patient/staff ratio is seen as an easily observed and objective variable by which to describe a treatment program or unit. However, Gunderson argues that the patient/staff ratio is a variable that is descriptive in nature and "thus is not inherently related to therapeutic process" (p. 238). Using Gunderson's (1978) argument, it is not surprising that Kellam, Goldberg, Schooler, Berman, and Schmelzer (1967) failed to establish a strong relationship between patient/nurse ratio and treatment outcomes in their study of treatment programs for patients diagnosed as schizophrenic. Schroder and Washington (1982) speak to the issue of basing a nurse classification system on the patient/ staff ratio of II numbers alone." The elements of a classification system (i.e., staffing philosophy, staffing objectives, quality assurance) are useful in defining different aspects of the delivery system. It supports what we already know: Staffing and nursing is more than staff/patient ratios, and the delivery system needs to be based on more sound theory than numbers alone. (p. 123) Treatment programs and their relationship to the type of 23 organization structure of the institution constitute another variable that should be taken into consideration in planning for nurse staffing. "Nursing care is inextricably dependent on the environment in which nursing activities take place (Young et al., 1980, p. 23). Only one article was found that addressed the issue of nursing in relation to the environment of care. Bishop (1983) describes an organizational structure of a psychiatric hospital which uses "adhocracy" to determine treatment team composition. The manner in which the teams meet individual patient needs is briefly described in the context of types of psychiatric nursing units. The lack of specific literature with regard to the relation­ship of treatment program and nurse staffing is reflected in the rela­tive lack of literature regarding nursing patient classification systems for psychiatric nursing. The number of articles describing psychiatric nursing patient classification systems represents a small proportion of the nursing literature in the area of nursing patient classification systems. A basic assumption of the present study is the inclusion of treatment program effect in developing nurse staff­ing patterns for psychiatric units is crucial if we are to adequately account for the variables that influence staffing. 24 Conceptual Framework There is general agreement in the literature that nursing patient classification systems have not worked well in psychiatry (Aydelette, 1973; Schroder & Washington, 1982). In reviewing the "state of the art,1I Giovannetti (1978) states: liTo date, there does not exist a widely used or accepted patient classification system for psychiatric patients. Although several attempts at development have been made, the obstacles appear to be more formidable than those in other speciality areas" (p. 61). Some of the obstacles identified by this researcher during years of clinical and administrative experience in psychiatric nursing stem from the nature of the clinical setting and the patients cared for in those settings. Psychiatric units have a long history of role blurring among members of the nursing staff and the interdisciplinary staff. Schroder and Washington (1982) note these phenomena as "purposeful" as well as "accidental" blurring of roles due to emphasis on the team concepts, heavy use of aides and technicians, and the nature of patient behaviors. IIFor example, when a patient is disrup­tive or presents an element of physical danger, we look for a large male employee, whether he is or isn't a nurse often doesn't matter" (pp. 113-114). Another description of patient behaviors which confounds the accurate assessment of categories and hours of care is the patient who manipulates the environment. On most classification indicators they register as minimal care patients, yet demand hours of staff time with constant requests, arguments, and so on. 25 Another confounding aspect in trying to determine nursing care needs stems from the number of group activities often involving other disciplines, and the number of activities that occur off the patient unit. The attempt to study the relationship between treatment unit programs and nurse staffing suggests another confounding variable in the determination of needed care levels. It is the premise of this researcher that treatment program activities as determined by medicine and/or other disciplines affect the level of nursing staff needed. Although it is a frequent occurrence in hospitals, the consequences of program development in any inpatient area are often not considered in terms of the impact on the manpower needed to staff the program. McClure and Nelson (1982), in their discussion of nurses' roles in hospitals, address the issue of the impact of other discipline activities in nursing. They identify an important variable inherent in the increasing acuity of illness of patients in hospitals, the "specialization phenomenon": Technologic advances have effected increased medical specialization, which in turn has led to increased nursing specialization and concomitantly to the specialization or segregation of patient care areas. • . . Critical to the responsibility of the nurse is the articulation of nursing and medical intervention. (pp. 62-63) Definitions Nursing Staffing Terminology Definitions of the nurse staffing terminology were taken from Young et al. (1980). Staffing levels refer to the gross number of nursing personnel 26 designated for a given area, such as an inpatient unit or a medical/ surgical service. Staffing levels may be presented as total nursing hours required, as number of full-time equivalents, with no attempt made to specify skill levels. Staffing ratios refer to the number of nurses per patient day, or the nursing hours per patient day, required to provide care. Staffing patterns refer to the mix or ratio of professional to nonprofessional nursing personnel for a specific unit, nursing service, or facility. Organizational mode is used to describe the manner in which patients or care activities are distributed among nursing personnel. In general, it refers to the administrative structure in the operation of a nursing unit for the delivery of care, such as team or primary nursing. Staffing methodology implies a formal mechanism or systematic procedures used to determine the number or mix of nursing personnel that are required to provide a predetermined standard of care to a specified patient population Patient Classification Concepts The following definitions are the same basic definitions used in the VA Psychiatric Nurse Staffing Study and are taken from Giovannetti and Thiessen (1983). Critical indicators of care are patient characteristics or descriptors used in the process of patient classification. Patient classification is the categorization or grouping of 27 patients according to an assessment of their nursing care requirements over a specified period of time. Patient classification methods are particular instruments or tools used to classify patients according to their nursing care requirements. Patient classification system refers to the process of identifying and classifying patients according to their nursing care requirements and to the quantification of the categories as a measure of nursing care time involved. Quantification is the operation of determining and assigning a time value or weight to each patient category for the purpose of estimating staffing requirements. In addition to these basic definitions, the modifier "nursing" has been added to the generic term "patient classification. 1I The nursing patient classification system terminology is intended to clarify the meaning of the classification system under discussion. The term patient classification refers to a wide variety of classifi­cation systems including medical, demographic, and those recently developed for the purpose of prospective payment. Nursing patient classification systems are the foundation for nursing staffing methodologies based on workload or patient requirements for nursing care. Treatment Program Variables The treatment program variables will be operationally defined using the descriptions of the specialized treatment unit 28 programs in the hospitals sampled in the OVAs. Five types of special­ized treatment programs were identified. The programs were a mixture of specific treatment modalities for patients with diverse diagnoses, if, for example, behavioral modification and intensive care and/or crisis intervention, plus those programs based on patient diagnoses and/or age, such as the units for chronic schizophrenic or brain­damaged patients and geriatrics. Each of the programs will be described using the following variables: (a) treatment modalities used; (b) the expected outcomes and/or goals of the unit; and (c) patient characteristics, such as sex, age, psychiatric diagnoses, length of stay, and number of read­missions. Study Hypothesis A significant amount of variance in the need for nurse staffing, as measured by direct care minutes, will be accounted for by the treatment programs of specialized psychiatric units over and above what will be accounted for by a nursing patient classification system. CHAPTER II METHODOLOGY The methodology used in the OVAs will be briefly described, including the setting of the study, the population, the data collec­tion procedures, and the nursing patient classification instruments. Next, the methodology used to conduct this study which is an exten­sion of the OVAs will be described with detailed descriptions of the specialized treatment units sampled. Synopsis of Veterans Administration Study Setting of Study Six test hospitals in the VA's multihospital system provided the setting for the original study. The hospitals were located in the following states: Tennessee, North Carolina, Ohio, Missouri, and California. The number of psychiatric units in the hospitals ranged from 2 to 15. All but 1 of the hospitals were affiliated with either a medical school or a nursing school or both. For purposes of dis­cussion, the hospitals are identified by the letters A through F (see Table 1). A more complete description of the hospitals and of the individual nursing units can be found in Appendix B. Population The VA has a total of 172 hospitals with a total of 88,354 operating beds in the national system. Of the 172 hospitals, 124 have Hospital A B C D E F Totals Tabl e 1 Study Hospitals Psychiatric Units Beds 2 45 8 173 3 76 4 120 6 250 15 485 38 1,149 30 Sample Units Beds 2 45 5 128 3 76 4 120 6 250 10 240 30 859 psychiatric beds for a total of 23,062 operating beds or 26% of the total beds in the system. There are 36 small psychiatric facilities with 50 or less beds, 51 medium-sized facilities with more than 50 but less than 200 beds, and 37 large facilities with over 200 beds. Psychiatric nursing staff account for 30% of the total nursing service budget. In the last quarter of fiscal year 1984, 9,664 full­time personnel were assigned to psychiatric nursing for a patient population of approximately 14,911. Sample A summary description of the demographic characteristics of the patients sampled can be found in Table 2. Total patients Male Female Age Range Median Distribution 20-29 30-39 40-49 50-59 60-69 65+ Acute Long-term Previous admissions 0 1 2 3 Multiple Table 2 Summary of Demographic Information Profile of Staff Mix in Six Test Hospitals Hospitals A B C D 59 136 87 97 58 136 85 95 1 0 2 2 24-86 25-84 24-77 22-70 46.6 47.8 45.2 56.3 6 10% 10 7% 12 14% 8 9% 15 25% 44 32% 31 35% 35 36% 10 17% 23 17% 6 7% 18 18% 17 29% 33 25% 17 20% 20 21% 6 10% 12 9% 11 13% 9 9% 5 9% 14 10% 10 11 % 7 7% 41 70% 61 45% 30 34% 66 68% 18 30% 75 55% 57 66% 31 32% 4 7% 18 13% 11 13% 15 16% 5 9% 22 16% 18 21% 20 21% 6 10% 29 21% 8 9% 10 10% 6 10% 13 10% 12 14% 2 2% 38 64% 54 40% 38 43% 50 52% E F 262 257 250 241 12 16 20-89 21-84 48.1 46.6 18 7% 26 10% 57 22% 78 30% 54 21% 50 20% 58 22% 40 16% 29 11 % 21 8% 46 17% 42 16% 74 28% 109 42% 188 72% 148 58% 7 3% 23 9% 48 18% 31 12% 41 16% 12 5% 26 10% 11 4% ~ 140 53% 180 70% Table 2 (Continued) A B C Length of stay Range 1 to 55 1 D-28 Y 1-96 D Median 16.5 D 828 0 26 D 45 days + 2 3% 53 39% 10 Staff mix RN 48% 33% 42% LPN 18% 6% 0% PNA 27% 54% 54% WC 6% 6% 4% Note. D = days; Y = years. Hospitals D 1-343 D 36.5 D 11% 19 20% 44% 0% 51% 5% E 1 D-25 Y 398 D 157 60% 33% 4% 60% 3% F 1- 694 D 114.2 D 107 42% 34% 5% 56% 5% W N Original Veterans Administration Study Methodology 33 The OVA's study goals were divided into three categories: (a) design of a psychiatric nursing staffing methodology and data collection in six test hospitals, (b) validation of the staffing methodology, and (c) data analysis and evaluation of study results. Four nursing patient classification instruments were used as a basis for a comparative study with the goal of selecting the most predictive nursing patient classification system as the basis for a staffing methodology for the VA. The classification instruments used were the Medicus Nursing Productivity and Quality System (NPAQ), Hartford Hospital's (Hartford) psychiatric patient classification, Intermountain Health Carels (IHC) classification system, and an experimental classi­fication system developed by the VA. See Appendix A for a more complete description of the instruments. Data collection was carried out in several phases. The first phase involved the training and orientation of VA nurses from the field who were selected to participate in the study. The majority of the VA nurses were psychiatric nurses. Each test hospital's study coordinator participated in the orientation. Orientation to the Medicus methodology, the only classification system the VA staff nurses used during the study, was accomplished by a 2-day orientation meeting. Following this orientation the VA nursing representatives returned to orient the nursing staffs of the study units. A l-hour videotape was produced by Medicus in an effort to standardize the orientation content; each of the test hospitals used a copy of the videotape for their inservice education on the use of the Medicus instrument. 34 The MESA research nursing staff included two psychiatric clinical specialists, one head nurse and one staff nurse with years of experience in psychiatric nursing, a graduate student in psychia­tric nursing, and a faculty member with an extensive background in nursing patient classification systems. The role of the MESA nurses was to classify all the patients in the study on the other three nursing patient classification instruments: IHe, Hartford, and VA. The MESA nurses had 2 days of orientation to the classification instruments included clinical testing of the instruments in the local VA hospital. In addition, they were oriented to the basic principles of worksampling and to the forms for data collection. During both orientations, interrater reliability testing was carried out with each of the patient classification instruments. The Medicus staff also conducted interrater reliability testing on NPAQ among the psychiatric nursing staffs at each of the six test hospitals and set the 90% level of agreement as an acceptable interrater score. Following the orientation phases, 2 trial weeks of patient classifications using the Medicus instrument were carried out in all six test hospitals by the psychiatric nursing staff. During the 2 trial weeks, nursing consultants from Medicus made site visits to all of the test hospitals to evaluate the appropriate and accurate use of the classification instrument. In addition, each hospital was asked to complete a unit survey by the head nurse outlining the major characteristics of the unit. 35 The second major phase of data collection was worksampling in the six test hospitals. The methodology followed in the worksampl­ing was based on the methodology developed in the San Joaquin project (U.S. Public Health Service, 1976). The basic elements of work­sampling included the "snap-shot" method of random observations of the nursing staff at lO-minute intervals. If the observation was of a direct care activity, the observers recorded the activity of both staff and patient(s). The staff activity was recorded using the codes for direct care and the patient was identified by a code assigned for the study. The patient codes were sequential numbers assigned from a master patient list. Group activities were recorded on a supplemental worksarnpling sheet which was developed in order to allow for collection of more precise information. The supplemental sheet allowed the observers to record the number and categories of staff, i.e., regis­tered nurse, psychiatric nurse assistants, the number of patients, and the type of group activity. Worksampling was carried out over a 7-day period in each of the six test hospitals. Work activities were coded and recorded on standard forms. Codes were assigned for direct care, indirect care, unit management, and personal time. For definitions of the direct, indirect, unit management, and personal time variables, see Appendix A. In addition to the worksampling activities, the head or charge nurse of each unit was asked to complete a questionnaire regarding ~er assessment of the adequacy of staffing and patient services during the shift (see Appendix A). All of the study forms were completed each shift by either the observers or in the case of the unit 36 questionnaire, the nursing staff. Patients were classified by the research staff each shift in the first four hospitals sampled and once a day in the last two hospitals. All sample patients were classified using the VA, Hart­ford, and IHC instruments. The research staff gathered data for the classifications from the nursing staff, observation of the patient, the patients' chart, and kardex. Outcomes A major outcome of the VA study was the development of a new nursing patient classification instrument. The instrument, MESA Psychiatric Patient Classification (MEPP), was constructed using a regression analysis of the 53 care indicators contained in the four classification instruments tested during the study. The criterion variable was the direct time spent with each patient and the predictor variables were the responses to the 53 indicators. Five indicators accounted for an ~ of .193. After the five indicators were entered, the remaining indicators accounted for a negligible increase in the variance. The five indicators had face validity in the opinion of the research staff and this researcher. All of the indicators account for activities that consume a great deal of nursing time in the direct care of patients. From the regression equation, MEPP was developed into a modified San Joaquin format. The instrument is found in Appendix C. MEPP was considered to be superior to the other four patient classification systems based on the following criteria: 1. The instrument accounted for a greater proportion of the 37 variance than the other instruments. 2. The average hours per patient day for each category were discrepant and the differences between the categories were statis­tically significant. 3. It was predictive of the care given to patients (Veterans Administration, 1985). An additional outcome of the VA study that is interesting to note was the recommendation that the percentage of registered nurse staff be increased throughout the system. This outcome was supported by data obtained on the unit evaluation questionnaire and the staffing profiles for the unit. Study To Determine Impact of Treatment Progranl On Nurse Staffing Sample The sample used for this study consisted of 11 specialized nursing units and 18 general psychiatric units. The nursing units in Hospital A were originally considered as two separate units but were later combined for the purposes of data collection and data analysis. The general psychiatric units were located in all six test hospitals. The specialized units were located in four of the hospitals. General Nursing Units The general psychiatric nursing units had no predominant treatment approach that determined the activities on the unit. A wide variety of eclectic treatment modalities were used, the pa­tients had a wide range of diagnoses, and the lengths of stay were 38 relatively short. (See Appendix B for general unit descriptions.) Specialized Nursing Units The purpose of the current study was to determine the influence of treatment program on nurse staffing needs. Therefore, detailed descriptions are given for each of the specialized nursing units in­cluding their treatment programs. Behavioral Modification Units Three of the units utilized a behavioral modification treat­ment program. The units are identified by the same codes used in the OVA's. Unit 43. The unit was located in Hospital E. It was a 50- bed unit which had an average daily census of 49 patients. The average number of admissions per day was 2. The most common psychiatric diagnoses of patients were schizophrenia, personality disorders, and manic-depressive illness. The unit did not admit patients with medi­cal problems that required skilled nursing care. The nursing staff has commented that it takes more staff to supervise patients for the reward system. Table 3 gives a demographic description of the sample patients. Unit 51. The unit was located in Hospital F. It had a bed capacity of 15 with an average daily census of 14 patients. The average number of admissions per day was less than 1. The most common psychiatric diagnoses of patients were chronic paranoid schizophrenia with history of assaultive behavior and organic brain syndrome. Patients with medical problems were admitted. The unit was called a Sex Male = 48 Female = 2 Age Range = 20-61 years Median = 41.5 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Table 3 Unit 43 Range = 12.0 days to 265.0 days Median = 96.3 days 45 days + = 39 or 78% Note. Total study patients = 50. Age 20-29 30-39 40-49 50-59 60-64 65+ N 5 16 18 9 2 0 Total 14 36 1 6 7 5 31 39 Percent 10 32 36 18 4 0 Percent 28 72 2 12 14 10 62 40 behavioral modification unit and the treatment program had both a token economy and a level system. The nursing staff commented that they have to have adequate staffing in order to provide close supervi­sion for all the patients. Table 4 gives a demographic description of the sample patients. Unit 53. The unit was also located in Hospital F. It had a bed capacity of 30 with an average daily census of 29 patients. The admissions to the unit were restricted by the treatment team and averaged less than 1 per day. The most common psychiatric diagnoses were schizophrenia, biopolar disorders, and depression. Patients with medical problems requiring skilled nursing care were admitted to the unit. Behavioral modification was the treatment program for all patients. The unit was called a treatment refractory unit and admitted patients who have had repeated, frequent hospitalizations during the past 2 years or who had been patients on inpatient units without notice­able progress for a long period of time, i.e., 2-25 years. See Table 5 for a demographic description of the sample patients. Intensive Care Units There were two intensive care units in the sample. Unit 45. The unit was located in Hospital E. It had a bed capacity of 9 with an average daily census of 2. The average number of admissions per day was 1. The most common psychiatric diagnoses were drug and alcohol abuse, antisocial personality, and affective disorders. Patients with medical problems, such as cardiac disease, diabetes, and emphysema, were admitted to the unit. The major Sex Male = 15 Female = 0 Age Range = 24-66 years Median = 40.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Table 4 Unit 51 Range = 19.0 days to 331.0 days Median = 128 days 45 days + = 13 or 87% Note. Total study patients = 15. Age 20-29 30-39 40-49 50-59 60-64 65+ N 2 7 3 2 0 1 Total o 15 o o o o 15 41 Percent 13 47 20 13 0 7 Percent o 100 o o o o 100 Sex Male = 28 Female = 1 Age Range = 24-70 years Median = 42.6 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Mul t"j pl e Length of stay Range = 7.0 days to 1.9 years Median = 189 days 45 days + = 26 or 90% Table 5 Unit 53 Note. Total study patients = 29. Age 20-29 30-39 40-49 50-59 60-64 65+ N 5 8 9 3 3 1 Total o 29 o o o o 29 42 Percent 17 28 32 10 10 3 Percent o 100 o o o o 100 43 treatment program of the unit was crisis intervention using a number of treatment approaches including milieu therapy, seclusion and restraint, behavioral modification, and individual therapy. The nursing staff also responded as a psychiatric emergency team through­out the hospital. See Table 6 for a demographic description of the patients in the sample. Unit 54. The unit was located in Hospital F. It had a bed capacity of 15 with an average daily census of 14. The average number of admissions per day ranged between 2 and 3. The most common psychiatric diagnoses of patients were manic-depressive, paranoid schizophrenia, character disorders, and poly-substance abuse. Pa­tients with medical problems were admitted including patients in withdrawal from alcohol or drugs. The treatment approaches include crisis intervention, behavioral modification, seclusion, and one-to­one therapy. See Table 7 for a demographic description of the sample patients. Geriatric Units There were two geriatric units in the sample. Unit 34. The unit was located in Hospital D. It had a bed capacity of 30 with an average daily census of 17 patients. The average number of admissions per day was less than 1. No patients were admitted who are younger than 60 years. The most common psychia­tric diagnoses of patients were depression with psychotic features, bipolar affective disorders, and schizophrenia. Patients were admitted who had chronic medical problems. The treatment program was Sex Male = 6 Female = 0 Age Range = 28-60 years Median = 37.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 4.0 days to 44.0 days Median = 15.0 days 45 days + = 0 or 0% Table 6 Unit 45 Note. Total study patients = 6. Age 20-29 30-39 40-49 50-59 60-64 65+ N 1 4 0 0 1 0 Total 5 1 o o 2 o 4 44 Percent 16.5 67 0 0 16.5 0 Percent 83 17 o o 33 o 67 Sex Male 22 Female = 3 Age Range = 26-75 years Median = 43.6 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 days to 43.0 days Median = 11.6 days 45 days + = 0 or 0% Table 7 Unit 54 Note. Total study patients = 25. Age 20-29 30-39 40-49 50-59 60-64 65+ N 2 10 6 4 1 2 Total 11 14 o 2 1 o 22 45 Percent 8 40 24 16 4 8 Percent 44 56 o 8 4 o 88 46 individualized but group activities constitute the most predominant therapeutic approach. The activities include senior citizen outings, recreation, exercise, swimming, bowling, a nutrition teaching group, and a coping skills group. See Table 8 for a demographic description of the sample. Unit 41. The unit was located in Hospital E. The bed capacity was 50 and the average daily census was 48 patients. The average number of admissions per day was less than 1. No one under age 50 was admitted to the unit. The most cornman psychiatric diagnoses of pa­tients are dementia and chronic schizophrenia. Patients with medical problems requiring skilled nursing care were not admitted to the unit. The predominant treatment aproach was resocialization and reality orientation and consisted of a variety of group activities scheduled 4 times a day, 5 days a week. See Table 9 for a demographic description of the patients in the sample. Chronic Schizophrenia Units There were two units for chronic schizophrenic patients in the sample. Unit 11. The unit was located in Hospital B. It had a bed capacity of 30 with an average daily census of 26 patients. Admissions to the unit were restricted by the treatment staff to patient meeting the admission criteria and occur seldom. The unit was a IIrehabilitation unit" for chronic schizophrenic patients. Patients with medical diagnoses which required skilled nursing care were admitted to the unit. The predominant therapeutic approaches Sex Male = 15 Female = 0 Age Range = 56-70 years Median = 62.5 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Table 8 Unit 34 Range = 4.0 days to 343.0 days Median = 65.0 days 45 days + = 6 or 40% Note. Total study patients = 15. Age 20-29 30-39 40-49 50-59 60-64 65+ N 0 a 0 2 7 5 Total 8 7 1 1 3 1 9 47 Percent 0 0 0 13 53 34 Percent 53 47 7 7 20 6 60 Sex Male 48 Female = 0 Age Range 51-88 years Median = 64.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Table 9 Unit 41 Range = 2.0 days to 23.5 years Median = 2.5 years 45 days + = 45 or 90% Note. Total study patients = 48. Age 20-29 30-39 40-49 50-59 60-64 65+ N 0 0 0 13 14 21 Total o 48 o 4 16 7 21 48 Percent 0 0 0 27 29 44 Percent o 100 o 8 33 15 44 49 included therapeutic milieu, behavioral modification, reality orienta­tion, and group and medication therapy. See Table 10 for a demo­graphic description of the patients in the sample. Unit 46. The unit was located in Hospital E. It had a bed capacity of 50 with an average daily census of 48 patients. The average number of daily admissions was less than 1. The patients on the unit had all been diagnosed as schizophrenic. Patients with medical diagnoses requiring skilled nursing care were not admitted to the unit. The predominant therapeutic approaches were milieu therapy with a great deal of hospital and outside community involvement. Group activities are a major focus for the unit and include patient council, women's group, remotivation, reality orientation, and therapy. Electro-schock therapy is also used for selected patients. See Table 11 for a demographic description of the sample patients. Brain Damage Units There were two units in the sample for patients with brain damage. Unit 18. The unit was located in Hospital B. It had a bed capacity of 28 with an average daily census of 15. Admissions were restricted to patients meeting the admission criteria and viewed by the treatment team as good candidates for the unit's approach to treatment. Admissions occurred only once or twice a year. The unit was a specialized unit for brain-damaged patients. The patients' diagnoses were all chronic brain syndrome due to a variety of etiologies. Patients with secondary medical diagnoses requiring Sex Male = 25 Female = 0 Age Range = 30-78 years Median = 51.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Table 10 Unit 11 Range = 6.0 months to 28 years Median = 10.5 years 45 days + = 25 or 100% Note. Total study patients = 25. Age 20-29 30-39 40-49 50-59 60-64 65+ N 0 2 8 11 3 1 Total o 25 1 2 2 7 13 50 Percent 0 8 32 44 12 4 Percent o 100 4 8 8 28 52 Sex Male = 44 Female = 4 Age Range = 28-89 years Median = 48.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Table 11 Unit 46 Range = 11.0 days to 25.0 years Median = 3.5 years 45 days + = 47 or 98% Note. Total study patients = 48. Age 20-29 30-39 40-49 50-59 60-64 65+ N 4 8 14 15 2 5 Total 8 40 o o 3 6 39 51 Percent 8 18 29 31 4 10 Percent 17 83 o o 6 13 81 52 skilled nursing care were admitted to the unit. The predominant therapeutic approach was constant reality orientation. Family meet­ings were held. Behavioral modification was used as well as individual and group therapy. Group activities were all structured and occurred on a daily basis. See Table 12 for a description of the sample patients. Unit 52. The unit was located in Hospital F. It had a bed capacity of 37 with an average daily census of 25 patients. The average admissions per day were 2. The most common psychiatric diagnoses were dementia, Alzheimer's disease, and depression. Patients frequently had secondary medical diagnoses that required skilled nurs­ing care. The predominant therapeutic approach was reality orienta­tion and resocialization. One-to-one attention and supervision of patients was the predominant activity of the unit. Group activities included a heritage group, a memory clinic, a group for significant others, after-care clinics, and ward government. See Table 13 for a demographic description of the patient included in the sample. Data Analysis Utilizing the data collected in the original VA study, it was possible to compare the 11 specialized units with five different types of treatment programs, the 18 general psychiatric units, and the direct nursing care hours obtained from worksampling in the VA study to determine the impact of treatment program on the need for nursing staff. Patients were reclassified according to the five indicators on Sex Male = 15 Female = 0 Age Range = 27-84 years Median = 53.5 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Table 12 Unit 18 Range = 34.0 days to 1.0 years Median = 140.0 days 45 days + = 13 or 87% Note. Total study patients = 15. Age 20-29 30-39 40-49 50-59 60-64 65+ N 1 4 0 3 4 3 Total o 15 1 2 3 4 5 53 Percent 6 27 0 20 27 20 Percent o 100 6 13 20 27 33 Sex Male = 25 Female = 4 Age Range = 60-84 years Median = 71.4 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 4.0 years Median = 125.0 days 45 days + = 17 or 59% Table 13 Unit 52 Note. Total study patients = 29. Age 20-29 30-39 40-49 50-59 60-64 65+ N 0 0 0 0 3 26 Total 9 20 9 6 o o 14 54 Percent 0 0 0 0 10 90 Percent 31 69 31 21 o o 48 55 the newly developed MEPP classification instrument. A computer algor­ithm was developed which searched the database for all four classi­fication forms for each patient. The scores received on each of the five indicators were summarized and a category of care was assigned by the computer program to each patient based on the sum of the five MEPP indicators. A hierarchical regression analysis was performed entering the variables in this order: (a) the criterion variables, (b) the new MEPP patient classifications, (c) the direct care minutes for each of the new MEPP categories, (d) the shift and then the predictor variable, and (e) program type. In addition, the interaction of MEPP classifications and treatnlent program types was analyzed for possible effect. The SAS statistical package was used to run the data analyases. CHAPTER III RESULTS The study hypothesis was as follows: A significant amount of variance in the need for nurse staffing, as measured by direct care minutes, will be accounted for by the treatment programs on specialized psychiatric units over and above what will be accounted for by a nursing patient classification system. The mean direct nursing care time in minutes for each of the four MEPP classification categories and each treatment program are presented in Table 13. Table 14 presents the results of the hierarchical regression analysis. Shift and MEPP care classification, regardless of program type, were entered into the equation first because it was the objective of this study to determine the degree to which psychiatric treatment program was able to predict nursing care over and above that attribut­able to a nursing patient classification system. Program was entered next and then the interaction of program and care classification. The interaction of program and care classification was considered in view of the possibility that both program placement and placement according to dependency on nursing care were two different ways of classifying the same patient. The interaction indicates whether the factors had a differential affect on direct care. The number of observations in the data set used for the Category 1 2 3 4 Category 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 57 Table 13 Direct Care Means in Minutes of MEPP Classification Categories and Treatment Programs N Means MEPP classification 5,489 4,940 1,504 576 Program N Means treatment programs General 3,876 Behavioral modification 623 Geriatric 284 Chronic schizophrenic 574 Brain damaged 132 General 3,080 Behavioral modification 529 Geriatric 529 Chronic schizophrenic 500 Brain damaged 290 Intensive 12 General 501 Behavioral modification 264 Geriatric 305 Chronic schizophrenic 105 Brain damaged 180 Intensive 149 General 140 Behavioral modification 119 Geriatric 51 Chronic schizophrenic 10 Brain damaged 99 Intensive 157 Direct care in minutes (per 24 hours) 21.888309 33.096028 58.821171 108.936974 Direct care in minutes (per 24 hours) 23.489900 15.992546 27.309255 15.568144 18.505899 34.617380 34.434827 30.743620 25.359157 26.231181 175.985400 60.134548 59.256858 4·1 .659883 58.079392 70.978965 74.597374 102.542639 91.803392 74.254210 73.259630 129.149008 128.419178 Table 14 Source Table and Hierarchical Regression Results Source df Sum of squares Mean square Model 24 7955145.8185495 331464.40910 Error 12,484 23634705.4473563 1893. 199731 Corrected total 12,508 3189851 .2659075 Source df Sum of squares F value Shift 2 2190916.060332 578.63 MEPP class 3 4984131 .626003 877.55 Program 5 352461.190859 37.23 MC1ass * Program 14 427636.941355 16.13 F value 175.08 PR > F 0.0001 0.0001 0.0001 0.0001 PR > F R2 .07 .16 .01 .01 U'1 OJ 59 analyses was 12,509, representing 898 patients. The significant results have to be considered in light of the large data set and the actual size of the differences between means. Treatment programs were predictive of direct care nursing hours. However, although the £ value for treatment programs was sta­tistically significant at the £ < .0001 level, the treatment program actually accounted for approximately 1% of the variability in direct care hours. The interaction between treatment program and patient classification was also statistically significant at the £ < .0001 level but again only accounted for approximately 1% of the variability in direct care. Patient classification using the MEPP instrument and shift were the variables that were most predictive of the direct hours of care, accounting for 23% of the variability. The findings of the study, when combined with the findings of the OVA's, do add significantly to the body of nursing knowledge regarding psychiatric nurse staffing methodologies. Further testing could further refine the instrument in a variety of different treatment settings. Such testing should increase the validity and usefulness of the MEPP classification system. CHAPTER IV DISCUSSION The original research question regarding the impact of inpa­tient treatment programs on the need for nursing staff was answered by this study. For the units sampled in the VA hospitals, treatment program had only a minimal impact on the quantity of nursing staff needed to care for patients. Treatment programs accounted for approxi­mately only 1% of the variability in predicted versus actual hours of care for all patients. The statistically significant I ratios in the hierarchical regression table can best be explained as due to the extremely large data set. Clinical and common sense leads to the conclusion that although the data produced statistical significance the results are not clinically significant. Hays (1981) in his discussion of statis­tical significance and common sense describes the phenomena encountered "in this study. "Virtually any study can be made to show significant results if one uses enough subjects, regardless of how small the effect. II He further states that lIif the criterion of strength of association is applied ... it becomes obvious that little or nothing is actually contributed to our ability to predict one thing ~rom anotherll (pp. 264-266). A finding of 1% for treatment program effect contributed little to the prediction of direct care hours. Keppel (1982) explains one of the reasons for likelihood of statistical significance with very large samples. liThe greater the sample size, the greater the power and the more sensitive the experiment in detecting treatment effects in populations" (p. 70). There are a number of possible explanations for the lack of treatment program effect including the most obvious one that at the time the OVAs was conducted, there may have been insufficient staff on the specialized units and therefore no differences were found between the staff on those units as compared to the general units. Another possible explanation is that the treatment programs, as described by the staff, were not occurring due to a variety of 61 factors including lack of support staff, such as housekeeping person­nel, forcing the nursing staff to pick up their responsibilities, such as unit cleaning. The presence of students from a number of disciplines on some of the treatment units may have altered the rou­tine of unit activities by involving patients in lengthy interviews and so on. The data collection instruments may not have been sensi­tive enough to pick up subtle differences due to treatment programming. The most likely explanation for the lack of treatment program effect on nurse staffing needs in relation to the nursing patient classification system can be found in the ability of the classifica­tion instrument to accurately measure the need for nursing care regardless of other factors, such as treatment program. As demon­strated in the original VA study, the nursing classification instru­~ ent (MEPP) accounted for more variance in the direct care hours delivered by nursing staff than seven other factors that could con­ceivably be related to variations in nursing care. The seven factors 62 were: 1. Chronicity of the nursing unit: The designation of acute or chronic in terms of the patients treated on a nursing unit was determined by the staff of each of the test hospitals. Factors considered in the determination were the average length of stay, number of readmissions, diagnoses, and unit goals. 2. Percentage of registered nurse staff: The percentage of registered nurses ranged from 27% to 54% with an average of 36%. 3. Percentage of socialization groups: The group activities which were coded as socialization are exemplified by escort groups, general dayroom activity groups, such as card playing and television watching. The percentage of socialization groups ranged from 3% to 33% with an average of 18%. 4. Days since admission were calculated for each patient for the current admission. 5. Patient chronicity as defined by the VA: Acute equals a length of stay of 45 days or less. Chronic is defined as a length of stay longer than 45 days. 6. Nurses' judgment of the patient's chronicity: The head nurse on each unit was asked to categorize each patient as chronic or acute based on his or her assessment of the patient. 7. Hospital: The six test hospitals were different in size, location, amount of and kinds of nursing resources, types of patients admitted, and affiliations with teaching programs. The nursing patient classification instrument (MEPP) measures the variable needed for nursing care based on patients' dependency on 63 nursing care regardless of the type of hospital staff mix, length of stay, and type of treatment program. Assumptions and Limitations of Study A basic assumption of the study is that if there is a treat­ment program influence or effect of the predicted hours of nursing care time for each patient category the effect will be measurable. The limitations of the study include the more general limita­tions of nursing patient classification systems. The most significant limitation is that nursing patient classification systems measure only current practice and therefore tend to formalize the status quo. Even though the MEPP classification instrument was demon­strated to be statistically valid, caution must be exercised however in assuming that because a nursing patient classification system is considered statistically valid, it measures the needs of patients. When validity is referred to in terms of nursing patient classification systems, it more properly should be referred to as predictive validity. Predictive validity refers to the extent to which established hours of care reflect actual care provided. The established hours of care can be set through a variety of methods including worksampling, but once the hours of care for each care category are established, they should continue to reflect the actual care given. Monitoring of nursing patient classification systems for both reliability and valid­ity is crucial to the successful use of the systems. Giovannetti and Mayer (1985) describe ways of successfully monitoring nursing patient classification systems as well as the educational processes required 64 for nursing staff to understand and successfully implement any system. The construction of the MEPP classification instrument intro­duced another confounding element and possible limitation. Three of the indicators on the MEPP instrument concern what may be considered treatment variables, i.e., unit privilege, restraints, and one-to-one observation. It may be that the MEPP instrument measures individual patient needs and treatment variable simultaneously. Another limitation of the study is the determination of treat­ment program on nurse staffing using only direct care times. All nurse staffing programs must account for the amount of time staff spend in indirect unit management and personal activities. In addition, the study is limited by the sample of only VA hospitals with relatively few treatment programs identified as involv­ing entire nursing units. The treatment programs are not well defined and the variables used to describe them are gross measures of treatment activity and influence. Another series of questions can be raised: Does the type of patient admitted to this chronic schizophrenic treatment unit deter­mine the type of treatment program or was the program established to treat these patients on one unit in the institution in order to centralize the care of hostile and aggressive patients? How does the program of care influence the nurse staffing for the unit? How and by whom was the decision made to create the unit? Were the conse­quences of the decision considered in terms of the effect of nurse staffing? Does the unit require a different staffing pattern than general psychiatric units? Is the staffing determined by the unique 65 characteristics of the unit or by the patient's individual need for nursing care or both? Finally, how many nurses are needed to give the level of care required and what basis was used for making that deter­mination? The influence of treatment programs on the nursing patient classification system examined in this study was minimal in terms of the numbers of staff required. However, the influence of treatment programs on the quality and categories of nursing staff was not examined and the findings of the study should not be interpreted to mean that treatment program has no effect on the overall staffing pattern for psychiatric nursing. A conclusion can be drawn that if treatment programs could have been demonstrated as having a real effect on staffing needs, a significant new variable would have been identified. The variable of treatment program as a predictor of staffing would then need to be accounted for in any classification scheme developed for psychiatric facilities using a treatment program model for one or more of the inpatient units. Implications for Nursing Since 95% of all care rendered to patients in hospitals is given by the nursing staff (American Hospital Association, 1980), the implications of staffing methodologies based on workload measure such a nursing patient classification system are far-reaching. These implications are concerned with two major areas: provi­sion of adequate staffing to meet patient needs for care and the 66 determination of those needs. Young et al. (1980) assert that it is a: • · · general concept that patient needs although variable can nevertheless be identified quantitatively, and that models based on patient assessment and classification schemes can be developed that predict requirements for care. These predic­tions, in turn, form the basis for a more logical and effective allocation of nursing resources. (p. 2) Models for staffing designed to meet patient needs are of great importance to nursing service administration as demonstrated by the estimate that $15 million is spent by hospitals per year on nurse staffing studies (Vaughan & Macleod, 1980). Implications for Further Studies Nursing patient classification systems provide a basis for determining staffing patterns both quantitatively and qualitatively. The amount of nursing staff required to provide patient care is a basic element in any nursing service program. The levels or types of nursing service personnel needed to provide care are based on nursing standards, departmental and institutional philosophy, budget constraints, availability of recruits, treatment programs, and philosophies and quality assurance standards. Although the linkage between nursing patient classification systems and professional nursing judgment and productivity, nursing standards and quality assurance have been explored by a number of authors, additional studies are needed to enrich the knowledge base for nursing practice and nursing administration. Williams and Murphy (1979) studied the relationship between objective measures of staffing adequacy, the patient services provided under various staffing conditions, and the charge nurse's subjective judgments of both of these elements. 67 Outcome criteria and a nursing patient classification system in community health are described by Daubert (1979). Giovannetti and Mayer (1985) describe the linkage of nursing patient classification systems and nursing care standards and quality assurance processes. The linkage between standards quality and classifications is an area which needs to be developed in order to respond to the concerns regarding the impact of regulations regarding classification systems, such as DRGs, on the quality of patient care. Perhaps one of the most important and relatively new uses of the data obtained from nursing patient classification systems is the use of the data by nursing service administrators in the unbundling of nursing costs from the per diem charges to inpatients. The importance of valid nursing patient classification data in costing out nursing for all areas of care cannot be underestimated, especially in view of the current revolution in health care. The effective management of patient care resources under prospective payment systems requires new knowledge and a comprehensive and integrated patient care and financial information system. . . . To have a complete picture of hospital resource utilization, it is necessary to correlate DRGs (a medical classification system) with a nursing patient classification system (NPCS) that indicates the nursing hours required to meet the patients' care needs. (Sovie et al., 1985, p. 90) A valid nursing patient classification instrument for psychia­tric nursing will provide a basis for nursing administrators to unbundle the nursing costs from the per diem costs in providing psychiatric inpatient care. 68 Sovie et al. (1985) used the Rush Medical Center's medical-surgical and psychiatric nursing patient classification instrument in a correlational study of nursing patient classification, DRGs, other significant patient variables, and total costs of patient care in their year-long study involving 24,879 patients. Conclusions from their study included the following: 1. Unit specific assignments of nursing care hours to a four­category system of nursing acuity adequately can account for the variations in the nursing care for all patients in the hospital. 2. The most common percentage of average direct nursing costs in the average room costs fall within the range of 18% to 24%. 3. Nursing patient classification data coupled with DRGs enabled a budget prediction that reflected 87% of the actual adjusted expenditures. Thompson (1984) argues for the research strategy of extending existing staffing models to create nursing time estimates for the total length of hospitalization for each patient. Nursing patient classification systems are viewed by Thompson as staffing algorithms that hospitals can use to determine their nursing intensity and costs. He makes an eloquent case for costing out nursing in the prospective payment system: The DRG payment system is based on the premise that to contain total case costs, all important subsets of that cost must also be contained. If an important component of case costs (nursing costs) cannot be measured, it weakens the structure of the total program. (p. 54) Several recent research studies have attempted to correlate DRGs and nursing patient classification systems (McClain & Selhat, 69 1984; Nyberg & Wolff, 1984; Riley & Schaefers, 1983). The Health Care Financing Administration (HCFA) has funded at least two major attempts to determine the allocation of inpatient nursing resource use: (a) the original work in New Jersey by Caterinicchio and Davies (1983) that resulted in part in the development of a relative inten­sity measure (RIMS) as an allocation statistic and (b) the study in progress by the American Nurses Association's Research Center, "DRG Refinement for Nursing Care. 1I Edwardson (1985) sums up nursing's role in the current health scene: Federal policymakers seem convinced that hospital care can and must be delivered more efficiently .... As the largest hospital division, nursing is a principal target for cost reduction. As the employee group with primary responsi­bility for coordinating medical nursing and ancillary services, nurses can and do influence the hospital IS overall productiv­ity. As a professional group, nurses have an ethical obliga­tion to use scare resources wisely. As patient advocates, nurses have a responsibility to protect patients from ill­conceived cost-cutting measures. (p. 14) Sovie (1985) lists 10 strategies and approaches that can be used to successfully manage nursing resources in a constrained economic environment. Among her comments in describing a strategy for unbundling nursing from room charges is the recommendation to report nursing costs per DRG in terms of nursing hours per DRG. "Hours are comparable across all regions in the country, whereas costs are subject to regional wage and salary variables" (pp. 88-89). Psychiatric nursing patient classification systems for staff­ing models hold great potential for assisting professional nursing judgment in a wide variety of nursing service concerns. An objective, 70 logical staffing program can be developed that is defendable in view of the competition for scare hospital resources. The data obtained via a valid nursing patient classification system can be used to link nursing standards and quality of care with nursing costs. Kelly (1985) is quoted as predicting that by 1990 some form of prospective payment will be used in all health care settings. She shares the concern of the many nursing leaders that the quality of nurs i ng is es pec i all y vu 1 nerab 1 e in cost-cutt i ng pr'ograms. It behooves the profession to arm itself with the objective data needed to provide the best, most efficient, and economical nursing care to patients in the times to come. Much of the needed data has already been produced via the many studies on the development and use of nursing patient classification systems and staffing methodologies, nursing productivity, and quality assurance. The profession now has the responsibility to develop innovative ways of using the data in planning the strategies and approaches that will be necessary for economic and professional survival as well as for the assurance of an acceptable level of care for patients. It is hoped that the outcomes of the OVAs and this study will have added to the knowledge base of the profession in the area of psychiatric patient classification and provided a promising new nursing patient classification system (MEPP). APPENDIX A PATIENT CLASSIFICATION INSTRUMENTS, WORKSAMPLING CODES, AND UNIT QUESTIONNAIRE 72 Patient Classification Instruments The patient classification instruments used in the OVAs were the Nursing Productivity and Quality Instrument (NPAQ) developed by Medicus Systems, Incorporated, Hartford Hospital's (Hartford) instru­ment, Intermountain Health Carels (IHC) instrument, and an experi­mental instrument developed by the VA. Medicus NPAQ The Medicus NPAQ is currently one of the most widely used com­mercially marketed systems. The instrument was developed during 1977-1978. Initial developmental activities were focused on a set of descriptors or workload influencing patient needs. These descriptors served as a set of indicators for grouping patients into categories reflective of their requirements for nursing care (Medicus Systems, Incorporated, 1985). Based on the results of initial studies to test proposed indicator sets, 37 "relevantll indicators with clear and specific definitions were developed. The indicators were then tested over a 5-month period. The testing included concurrent independent classifications of patients by unit by project staff to determine the reliability of the indicators. Weights were then developed for each indicator uSing an expert panel of psychiatric nurses and Medicus staff. Following the development of a weighting system, validation of the proposed system was begun. Worksamp1ing using principles of industrial engineering was used as a validation methodology. The worksampling observer chose patients of each II patient type" from the most current patient classification sheet and observed the patient six 73 times an hour for the entire shift. A series of trial implementations of the system were then undertaken by Medicus. Modifications in the indicator set were made with the resulting 40 indicator classifications instrument used in the OVAs. The 40 indicators describe various nursing activities, such as one-to-one restriction, bathe with assis­tance, restraint application, and various patient characteristics, such as hyperactivity, delusional, and sensory deficits. The indicators are marked yes or no. The total patient score is then placed in one of four categories of care. Hartford Hospital The classification instrument developed by Hartford Hospital is relatively new and the use of it in the OVAs was the first outside of Hartford Hospital. The instrument was developed by the nursing service department with consultation from management engineering and an outside nursing consultant. The development of the tool has taken place over a 4-year period. Worksampling has been conducted to determine the predictive validity of the tool in its use at Hartford; the validity studies are still in progress. The tool has four indi­cators: activities of daily living, unit privileges, observation, and medications and is formated in a San Joaquin format with four patient categories. Intermountain Health Care The classification instrument used in the Intermountain Health Care (IHC) hospitals was developed in the early 1980s by the manage­ment engineering department of the corporation with consultation from 74 nursing service. Worksampling was conducted over a 6-month period in one of the larger hospitals to determine average quantification coef­ficients for each category. The instrument has three major clusters of activities labeled "assist,1I lIassessment/intervention," and lIunit status. II There are three to four descriptors in each cluster. For example, lIunit status" has descriptors: open unit-group neighborhood, open unit-unit status, and closed unit and IPCR. The instrument is in a San Joaquin format with four patient categories. Veterans Administration The experimental instrument, developed by the VA, was based on previous experience with a classification system that was not con­sidered by nursing service to be lIobjective enough ll to classify patients for staffing and budget purposes. The experimental instrument was a product of consultation between the nursing service department which includes nurse researchers and the management system services department. The instrument 'is divided into two sections, Section A and B. Section A has two indicators, IIbehavior" and "privileges,1I with three descriptors under each. Section B has four indicators, "basic hygiene bathing,1I IInutrition/feeding," "elimination," and "mobility." Each indicator has three descriptors. The instrument is in a San Joaquin format, with five patient categories. Worksampling Codes In the OVAs, codes for 29 nursing care activities were developed. Codes 1-14 were used for direct care activities; codes 21-35 were used for indirect care activities; codes 31-36 were used for unit management activities; code 37 was used for "other" activ­ities; code 41 was used for personal time; code 51 was used for unusual incidents; and code 99 was used for group activities. Code Definitions 75 Direct care was defined as those nursing activities that are patient centered and take place in the presence of the patient and/or family. Operationally, direct care activities included: 1. Communication (general) 2. Communication (with patient or family) 3. Medications 4. Patient hygiene (individual) 5. Patient hygiene (group supervision) 6. Patient movement (transport or escort) 7. Rounds or assist doctor or other with patient 8. Nursing rounds 9. Observe or supetvise (one patient) 10. Observe or supervise (group unstructured) 11. Vital signs/treatments and procedures 12. Structured therapy (individual) 13. Therapy, assist/supervise (more than one). Indirect care was defined as those activities which were away from the patient but were in preparation for in completion of direct nursing care. Operationally, direct care included the following activities: 1. Charting (checking chart of kardex) 76 2. Communications (regarding specific patients with others) 3. Preparing medications, IVs 4. Transcribing orders (charge slips). Unit related activities were defined as activities and tasks necessary for the general management coordination and organization of a nursing unit. Those functions related to the well-being of the patient population on the unit but not specific for a patient. Operationally, unit management activities included: 1. Cleaning, housekeeping tasks 2. Clerical activities 3. Communication with others (unit related) 4. Errands off unit 5. Meetings, inservice, unit reports 6. Supplies (check, restock). Personal activities were defined as those activities not directed toward patient care or unit management. Operationally, personal activities included: 1. Meal times 2. Coffee breaks 3. Nonproductive time 4. Socialization not related to work 5. Personal telephone calls. Unusual incidents were defined as occurrences which disrupted the normal routine of the unit. Operationally, the activities included such occurrences as fire drills, cardiac arrest, suicide, or psychia­tric emergency. HHd Nurse or Designated Charge Nurse: P/easa complet. at or toward the end of each shift. UNIT STAFFING I CARE EVALUATION QUESTIONNAIRE SHIFT:O E N UNIT: IT] DATE: CD rn rn Person completing form: Head Nurse RN LPN PNA Ward Clerk Other TOTAL 1. In general, did you feel that staffing for this shift was: More than adequate 5( ) Comfortable 4( ) Adequate 3 ( ) stan on Shift Full·Time Part·time Number Number Hours Barely adequate 2 () Inadequate 1 ( ) 77 2. If you checked barely adequate or 'nadequate, which one(s) of the following factors seemed to best describe the situation? (check the one(s) that apply.) 2.1 Above average number of patients needing extenSive nursing care, assistance, or surveillance (e.g., suicide precautions, one-to-one) 2.2 Not enough staff on duty. E.g., too few scheduled; scheduled but not on duty, or transferred to another unit. 2.3 Census fluctuations. E.g., above average number of admissions this shift or previous shift, or above average number of dismissals or transfers. 2.4 Not an optimum mix of personnel skill levels. 2.5 Above average number of patients needing escort by nursing to other departments. 2.6 Other (Please describe) 78 3. In your judgement If staffing was barely adequate or tnadequate. what would have best helped the sltua· tlon? (Check the one(s) that apply.) 3.1 Additional PNA all shift 3.2 Two additional PNA's all shift 3.3 Additional PNA part of shift 3.4 Additional RN all shift 3.5 Two additional RN's all shift 3.6 Additional RN part of shift 3.7 Additional LPN all shift 3.8 Two additlona' LPN's all shift 3.9 Additional LPN part of shift 3.10 WC all shift 3.11 Additional WC part of shift 3.12 Other (Please describe) 4. Please judge the following aspects of care according to your best knowledge of the nursing services given to at least 900;. of the patients during this shift. Consider factors such as safety. accuracy, patient comfort and satisfaction. and agreement with medical and nursing plans of care. Check the value that best applies using the following scale: 5 == Good 3 ;II: Satisfactory 1 == Poor 4 == More than satisfactory 2 - Less than satisfactory 4.1 Communications with patient andlor family (explanation of procedures, Instruction. support, assurance). 1 2 3 4 5 ____ __ 4.2 Medications, IV's (given as ordered and within time limits). 1 2 3 4 5 ____ __ 4.3 Patient hygiene (assistance with hygiene, nutrition, elimination). 1 2 3 4 5 ____ __ 4.4 Patient movement, transport, or escort. 1 2 3 .. 5 ____ __ 4.5 Participate with treatment team (e.g .• M.D .• social worker, psychologist) In planning & rounds, etc. 1 2 3 4 5 ____ __ 4.6 Nursing rounds, checks, symptom observation. 1 2 3 4 5 ____ __ 4.7 Observation or supervision of .patients. 1 2 3 4 5 ____ __ 4.8 Vital signs or treatments and procedures (taken as indicated or ordered). 1 2 3 4 5 ____ __ 4.9 Individual structured therapy. 1 2 3 ____ .. ____ 5 ___ _ 4.10 ASSist or supervise structured group therapy. 1 2 3 4 5 ____ __ 5. Please mark with an X the pace at which your staff worked during this shift. , 2 3 4 5 ____ __ Very Slow About Normal Extremely Rushed 6. Comments (see other side If neededl _______________________ _ Note. From Development of methods for determining use and effective­ness of nursing service personnel by U.S. Public Health Service, 1976, pp. 248-267. APPENDIX B DESCRIPTIONS OF TEST HOSPITALS AND GENERAL PSYCHIATRIC UNITS 80 Hospital A Demographic information: The number of operating beds in the psychiatric nursing service was 45. The beds were distributed between a locked and open unit. Unit 1 Bed capacity = 22 Average daily census = 19 Average admissions per day = 3 Patient characteristics: The most common psychiatric diag­noses of patients were schizophrenia, bipolar disorders, acute brain syndrome, acute and chronic alcoholism, borderline personality dis­orders, depression, and substance abuse. It was not the norm of the unit to admit patients with medical diagnoses that required skilled nursing care. The unit admitted patients requiring intensive nursing care, seclusion, and suicide precautions. The therapeutic approach is eclectic. Group activities include psychotherapy, medication education, orientation, and general health information. ALANON meetings are held as well as ward com­munity meetings. See Table 15 for demographic description of sample patients. Unit 2 Bed capacity = 23 Average daily census = 22 Average admissions per day = 3 Sex Male = 58 Female = 1 Age Range = 24-86 years Median = 46.6 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 55.0 days Median = 16.5 days 45 days + = 2 or 3% Table 15 Unit 1 Note. Total study patients = 59. Age 20-29 30-39 40-49 50-59 60-64 65+ N 6 15 10 17 6 5 Total 41 18 4 5 6 6 38 81 Percent 10 25 17 29 10 9 Percent 70 30 7 9 10 10 64 Patient characteristics: The most common psychiatric diag­noses were schizophrenia, bipolar disorders, chronic alcoholism, borderline personality disorders, organic brain syndromes, depres­sion, and substance abuse. Patients with medical problems that required skilled nursing care were not admitted to the unit. 82 Therapeutic programs and groups are the same as they are for the closed side of the unit (see Table 16). Hospital B Demographic information: Five units were included in the sample for a total of 128 operating beds. Three of the units were general psychiatric units. Unit 14 Bed capacity = 22 Average daily census = 20 Average admissions per day = 2 Patient characteristics: The most common psychiatric diag­noses of patients were schizophrenia, depression, manic-depression, borderline personality disorders, and organic brain syndrome. Patients with medical problems were admitted to the unit. The major treatment modality was an individualized approach using chemotherapy, activities, behavioral modification, and group therapy. See Table 16 for demographic description of sample. Sex Male = 29 Female = 0 Age Range = 25-78 years Median = 47.5 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions 0 1 2 3 Multiple Length of stay Range = 1.0 day to 14.7 years Table 16 Unit 14 Age N 20-29 2 30-39 9 40-49 5 50-59 7 60-64 1 65+ 5 Total 6 23 4 5 2 2 16 Median = 270.0 days without 3 long-term patients 29 days 45 days + = 6 or 20% Note. Total study patients = 29. 83 Percent 7 31 17 24 3 17 Percent 20 80 14 17 7 7 55 Unit 15 Bed capacity = 24 Average daily census = 22 Average admissions per day = 84 Patient characteristics: The most common psychiatric diag­noses were depression, borderline personality with alcohol or poly­drug abuse, schizophrenia, organic brain syndrome, and bipolar dis­orders. Patients with medical problems requiring skilled nursing care were admitted to the unit. The predominant therapeutic approaches were chemotherapy, psychotherapy, and biofeedback. Both individual and group therapy were utilized. See Table 17 for demographic description of sample patients. Unit 16 Bed capacity = 24 Average daily census = 24 Average admissions per day = 3 Patient characteristics: The most common psychiatric diag­noses were schizophrenia, bipolar affective disorders, organic brain syndrome, Alzheimer's disease, anxiety neurosis, personality dis­orders, and depression. Patients with medical problems requiring skilled nursing care were admitted to the unit. The predominant therapeutic approaches included behavioral modification, milieu therapy, group therapy, reality orientation, individual support therapy, medications, and family teaching. See Sex Male = 37 Female = 0 Age Range = 26-68 years Median = 40.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 65.0 days Median = 16.0 days 45 days + = 3 or 8% Table 17 Unit 15 Note. Total study patients = 37. Age 20-29 30-39 40-49 50-59 60-64 65+ N 5 18 7 5 1 1 Total 34 3 11 8 5 o 13 85 Percent 14 49 19 14 3 3 Percent 92 8 30 22 13 o 35 86 Table 18 for demographic description of sample patients. Hospital C Demographic information: The hospital had three psychiatric units with a total of 76 beds. All three units were included in the study. Unit 21 Bed capacity = 26 Average daily census = 23 Average admissions per day = 1 Patient characteristics: The most common psychiatric diag­noses were major depressions, schizophrenia, anxiety-obsessive­compulsive disorders, personality disorders, and drug abuse. Patients with medical problems requiring skilled nursing care were admitted to the unit. The predominant therapeutic approach was individual and group therapy. See Table 19 for demographic description of patients in sample. Unit 22 Bed capacity = 28 Average daily census = 23 Average admissions per day = 3 Patient characteristics: The most common psychiatric diag­noses were schizophrenia, alcohol and drug abuse, depression, and dementia. The unit admitted patients with medical problems requiring Sex Male = 30 Female = 0 Age Range = 25-73 years Median = 47.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 138.0 days Median = 26.0 days 45 days + = 6 or 20% Table 18 Unit 16 Note. Total study patients = 30. Age 20-29 30-39 40-49 50-59 60-64 65+ N 2 11 3 7 3 4 Total 21 9 1 5 17 o 7 87 Percent 6 36 10 23 10 13 Percent 70 30 3 17 57 o 23 Sex Male = 28 Female = 2 Age Range = 24-77 years Median = 44.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 2.0 days to 56.0 days Median = 21.0 days 45 days + = 2 or 6% Table 19 Unit 21 Note. Total study patients = 30. Age 20-29 30-39 40-49 50-59 60-64 65+ N 5 9 3 7 4 2 Total 12 18 7 5 4 5 9 88 Percent 17 30 10 23 14 6 Percent 40 60 23 17 14 17 30 89 skilled nursing care. The predominant therapeutic approaches included behavioral modification, electro-shock therapy, and group therapy. See Table 20 for demographic description of sample patients. Unit 23 Bed capacity = 22 Average daily census = 22 Average admissions per day = 2-3 Patient characteristics: The most common psychiatric diag­noses were personality disorders, chronic schizophrenia, organic brain syndrome, and depression. The unit admitted patients with medical problems requiring skilled nursing care. The predominant therapeutic approaches were behavioral modi­fication, group therapy, and reality orientation. See Table 21 for demographic description of sample patients. Hospital D Demographic information: All four psychiatric units were included in the study for a total of 120 beds. Unit 31 Bed capacity = 30 Average daily census = 20 Average admissions per day = Patient characteristics: The most common psychiatric diag­noses were schizophrenia and depression. The unit admitted patients Sex Male = 32 Female = 0 Age Range = 26-70 years Median = 44.7 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 days to 96.0 days Median = 30.0 days 45 days + = 5 or 16% Table 20 Unit 22 Note. Total study patients = 32. Age 20-29 30-39 40-49 50-59 60-64 65+ N 5 12 2 6 4 3 Total 10 22 4 5 1 6 16 90 Percent 16 38 6 19 12 9 Percent 31 69 12 16 3 19 50 Sex Male = 25 Female = 0 Age Range = 25-71 years Median = 47.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 71.0 days Median = 27.0 days 45 days + = 3 or 12% Table 21 Unit 23 Note. Total study patients = 25. Age 20-29 30-39 40-49 50-59 60-64 65+ N 2 10 1 4 3 5 Total 8 17 o 8 3 1 13 91 Percent 8 40 4 16 12 20 Percent 32 68 o 32 12 4 52 92 with medical problems requiring skilled nursing care. The predominant therapeutic approach is eclectic. See Table 22 for demographic description of patients. Unit 32 Bed capacity = 30 Average daily census = 15 Average admissions per day = 1 Patient characteristics: The most common psychiatric diag­noses were schizophrenia and depression. Patients with medical problems requiring skilled nursing care were not admitted to the unit. The therapeutic approach was eclectic. See Table 23 for demographic description of sample patients. Unit 33 Bed capacity = 30 Average daily census = 20 Average admissions per day = 1-2 Patient characteristics: The most common psychiatric diag­noses were bipolar disorders, schizophrenia, depression, and person­ality disorders. The unit admitted patients with medical diagnoses that required skilled nursing care. The predominant therapeutic approach was milieu therapy. See Table 24 for demographic description of sample patients. Sex Male = 24 Female = 1 Age Range = 23-61 years Median = 42.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 177.0 days Median = 26.0 days 45 days + = 4 or 16% Table 22 Unit 31 Note. Total study patients = 25. Age 20-29 30-39 40-49 50-59 60-64 65+ N 4 7 7 6 1 0 Total 18 7 o 6 3 1 15 93 Percent 16 28 28 24 4 0 Percent 72 28 o 24 12 4 60 Sex Male = 28 Female = 1 Age Range = 22-69 years Median = 41.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 137.0 days Median = 26.0 days 45 days + = 3 or 10% Table 23 Unit 32 Note. Total study patients = 29. Age 20-29 30-39 40-49 50-59 60-64 65+ N 2 14 6 5 0 2 Total 20 9 10 2 3 o 14 94 Percent 7 48 21 17 0 7 Percent 69 31 35 7 10 o 48 Sex Male = 28 Female = 0 Age Range = 24-56 years Median = 39.5 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 199.0 days Median = 29.0 days 45 days + = 6 or 21% Table 24 Unit 33 Note. Total study patients = 28. Age 20-29 30-39 40-49 50-59 60-64 65+ N 2 14 5 7 0 0 Total 20 8 4 11 1 o 12 95 Percent 7 50 18 25 0 0 Percent 71 29 14 39 4 o 43 Hospital E All six psychiatric units were studied for a total of 250 operating beds. Two of the units were general psychiatric units. Unit 42 Bed capacity = 50 Average daily census = 40 Average admissions per day = 2 96 Patient characteristics: The most common diagnoses of patients were schizophrenia, substance disorders, organic brain disorders, and personality disorders. On occasion patients with medical diagnoses requiring skilled nursing care were admitted to the unit. The predominant therapeutic approach was evaluation, treat­ment, and disposition. Electro-shock therapy was used weekly. Group activities were focused on multidisciplinary treatment team meetings, group therapy, orientation, and patient government. See Table 25 for a demographic description of the patients in the sample. Unit 44 Bed capacity = 41 Average daily census = 37 Average admissions per day = 2 Patient characteristics: The most common psychiatric diag­noses were schizophrenia, affective disorders, substance abuse, dementia, and personality disorders. Patients with medical problems were admitted to the unit. Sex Male = 60 Female = 4 Age Range = 25-82 years Median = 49.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 291.0 days Median = 46.0 days 45 days + = 13 or 20% Table 25 Unit 42 Note. Total study patients = 64. Age 20-29 30-39 40-49 50-59 60-64 65+ N 3 18 14 11 6 12 Total 25 39 3 25 9 4 23 97 Percent 5 28 22 17 9 19 Percent 39 61 5 39 14 6 36 98 The predominant therapy approaches were crisis intervention, milieu therapy, and individual therapy. Electro-shock was also used. Group activities included reality orientation, ward government, rap sessions, remotivation, and group therapy. See Table 26 ,for a demo­graphic description of the sample patients. Hospital F Demographic information: Ten of the 15 psychiatric nursing units were studied for a total of 240 operating beds. Six of the units studied were general psychiatric units. Unit 57 Bed capacity = 20 Average daily census = 20 Average admissions per day = 2 Patient characteristics: The most common psychiatric diag­noses were chronic paranoid schizophrenia, depression, manic-depres­sion, and personality disorders. Patients with medical problems were not admitted to the unit. The most predominant therapeutic approaches were behavioral modification, personal effectiveness training, relaxation, and group psychotherapy. See Table 27 for a demographic description of the sample patients. Unit 58 Bed capacity = 20 Average daily census = 20 Sex Male = 44 Female = 2 Age Range = 21-87 years Median = 49.0 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 210.0 days Median = 38.0 days 45 days + = 13 or 28% Table 26 Unit 44 Note. Total study patients = 46. Age 20-29 30-39 40-49 50-59 60-64 65+ N 5 11 8 10 4 8 Total 22 24 3 13 4 4 22 99 Percent 11 24 17 22 9 17 Percent 48 52 6 28 9 9 48 Sex Male = 22 Female = 3 Age Range = 21-70 years Median = 43.5 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 176.0 days Median = 47.6 days 45 days + = 10 or 40% Table 27 Unit 57 Note. Total study patients = 25. Age 20-29 30-39 40-49 50-59 60-64 65+ N 2 14 1 5 1 2 Total 7 18 2 4 1 4 14 100 Percent 8 56 4 20 4 8 Percent 28 72 8 16 4 16 56 Average admissions per day = 1 Patient characteristics: The most common psychiatric diag­noses were schizophrenia, borderline disorders, manic-depressive illness, and personality and affective disorders. Patients with medical diagnoses were not admitted to the unit. The predominant therapy approach was family therapy. The group activities are centered around family therapy or management groups. See Table 28 for a demographic description of the sample patients. Unit 60 Bed capacity = 20 Average daily census = 20 Average admissions per day = Patient characteristics: The most common psychiatric diag­noses were substance abuse, chronic schizophrenia, personality dis­orders, and bipolar disorders. Patients with medical diagnoses requiring skilled nursing care were admitted to the unit. 101 The predominant therapeutic approach was group therapy. See Table 29 for a demographic description of the patients in the sample. Unit 61 Bed capacity = 20 Average daily census = 18 Average admissions per day = 2 Patient characteristics: The most common psychiatric diag­noses were substance abuse, borderline personality, paranoid Sex Male = 21 Female = 2 Age Range = 22-66 years Median = 39.9 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 104.0 days Median = 35.0 days 45 days + = 8 or 35% Table 28 Unit 58 Note. Total study patients = 23. Age 20-29 30-39 40-49 50-59 60-64 65+ N 6 6 6 2 2 1 Total 6 17 o 1 3 1 18 102 Percent 26 26 26 9 9 4 Percent 26 74 o 4 13 4 79 Sex Male = 20 Female = 1 Age Range = 25-62 years Median = 43.5 years Distribution Acute or long-term (head nurse assessment) Acute Long-term Previous admissions o 1 2 3 Multiple Length of stay Range = 1.0 day to 102.0 days Median = 34.4 days 45 days + = 5 or 24% Table 29 Unit 60 Note. Total study patients = 21. Age 20-29 30-39 40-49 50-59 60-64 65+ N 3 7 2 7 2 0 Total 16 5 2 1 1 3 14 103 Percent 14 33 10 33 10 0 Percent 76 24 10 5 5 14 66 104 schizophrenia, and depression. The unit did not admit patients with medical diagnoses. There was no predominant therapeutic approach. Group activ­ities included a substance abuse group, a socialization group, and therapeutic community meetings. See Table 30 for a demographic description of the sample patients. Unit 62 Bed capacity = 30 Average daily census = 30 Average admissions per day = 1 Patient characteristics: The most common psychiatric diag­noses were depression, manic-depression, substance abuse, borderline personality, and posttraumatic stress syndrome. The unit did not admit patients with medical diagnoses. There