Case-mix measurement in medical rehabilitation

doi:10.1016/S0003-9993(95)80127-8

Archives of Physical Medicine and Rehabilitation

Volume 76, Issue 12, December 1995, Pages 1163-1170

https://doi.org/10.1016/S0003-9993(95)80127-8 Get rights and content

Abstract

Case-mix measurement offers a way to take patient characteristics into account in the determination of payment rates. This article begins with an overview of major case-mix measures across inpatient hospital and other institutional settings and describes ways to measure the suitability and relative strengths of these measures. It then briefly discusses issues of payment and the appropriateness of alternative case-mix measures to inpatient rehabilitation. The literature review extends back to the 1970s, thus preceding advent of the Diagnosis-Related Groups, which was the first major case-mix measure developed and implemented in a hospital setting.

References (40)

HeinemannAW et al.
Prediction of rehabilitation outcomes with disability measures
Arch Phys Med Rehabil
(1994)
StinemanMG et al.
Four methods for characterizing disability in the formation of Function Related Groups
Arch Phys Med Rehabil
(1994)
LinacreJM et al.
The structure and stability of the Functional Independence Measure
Arch Phys Med Rehabil
(1994)
FetterRB
DRGs: their design and development
(1991)
AshcraftMLF et al.
A psychiatric patient classification system: an alternative to diagnosis-related groups
Med Care
(1989)
FetterRB et al.
Case mix definition by diagnosis related groups
Med Care
(1980)
FriesBE et al.
Resource Utilization Groups
A patient classification system for long-term care
Med Care
(1985)
FriesBE et al.
Refining a case-mix measure for nursing homes: Resource Utilizations Groups (RUG-III)
Med Care
(1994)
HaradaN et al.
Development of a resource-based classification scheme for rehabilitation
Inquiry
(1993)
StinemanMG et al.
A prototype classification system for medical rehabilitation
(1994)

StinemanMG et al.

A case-mix classification system for medical rehabilitation

Med Care

(1994)

Federal Register

(1994)

International Classification of Diseases

Clinical Modification

(1993)

Federal Register

(1994)

CretinS et al.

Alternative Systems for Case Mix Classification in Health Care Financing

McGinnisGE et al.

Predicting charges for inpatient medical rehabilitation using severity, DRG, age, and function

Am J Public Health

(1987)

McGinnisGE et al.

Total charges for inpatient medical rehabilitation

Health Care Financing Rev

(1988)

StinemanMG et al.

Predicting inpatient rehabilitation length of stay

Arch Phys Med Rehabil

(1990)

HosekS et al.

Charges and Outcomes for Rehabilitation Care

JohnstonMV et al.

Cost-effectiveness of the Medicare three-hour regulation

Arch Phys Med Rehabil

(1986)

Cited by (23)

Factors influencing selection for rehabilitation after stroke: A questionnaire using case scenarios to investigate physician perspectives and level of agreement
2012, Archives of Physical Medicine and Rehabilitation
Citation Excerpt :
In several health services, more than 1 physician completed the survey; however, we did not have data on the total number of physicians working in rehabilitation in each health service. It has been recognized in the literature that the process of selection for rehabilitation may lead to inequities of access for patients with stroke.7 Evidence for variation in the rate of discharge from the acute hospital setting to nursing home care after stroke, after adjusting for casemix, has been demonstrated by Portelli et al.4 Ilett et al6 demonstrated variations in the proportion of patients discharged from acute hospitals to rehabilitation versus home or nursing home.
Kennedy GM, Brock KA, Lunt AW, Black SJ. Factors influencing selection for rehabilitation after stroke: a questionnaire using case scenarios to investigate physician perspectives and level of agreement.
To explore the key factors involved in decision making when selecting patients for rehabilitation after stroke and to examine the level of agreement among physician assessors regarding admission to rehabilitation.
Questionnaire.
Health services with rehabilitation units in Victoria, Australia.
Rehabilitation unit physicians.
Not applicable.
Questionnaire with 2 components: the clinical and nonclinical factors that influence decision making and clinical case scenarios.
Responses were received from 17 physicians from 12 of the 18 health services in Victoria. The most influential clinical factors listed by the respondents were prognosis, social supports, anticipated discharge destination, age, and anticipated length of stay. Key nonclinical factors were prioritization of internal health service referrals, patient's residence, and workforce capacity. Analysis of the clinical scenarios of patients with severe stroke showed that there was variability in the responses, with high levels of agreement for some cases and low levels for others. Almost all respondents agreed that prognosis was a key factor, yet, within the case scenarios, the reasons given for accepting or not accepting the patient demonstrated different opinions on the prognosis of the case presented.
The decision-making processes in selection for rehabilitation and the factors that influence that decision require further investigation to optimize the use and outcomes from rehabilitation resources.
Selecting Patients for Rehabilitation After Acute Stroke: Are There Variations in Practice?
2010, Archives of Physical Medicine and Rehabilitation
Ilett PA, Brock KA, Graven CJ, Cotton SM. Selecting patients for rehabilitation after acute stroke: are there variations in practice?
To investigate whether there were variations in practice in selection for rehabilitation after stroke, after adjustment for case mix.
Prospective multicenter audit.
Seven acute stroke units in metropolitan and regional Victoria, Australia.
Consecutive acute stroke admissions (N=616).
None.
Mobility Scale for Acute Stroke Score and Modified Barthel Index (MBI) scores for continence at day 3 poststroke, discharge destination from the acute hospital.
Data were analyzed for 616 stroke survivors. Considerable variability in the percentage of cases accessing inpatient rehabilitation was observed in severe stroke (27%–67%) and mild stroke (27%–73%). To assess adjustment for case mix, a multinomial logistic regression was conducted with the outcome variable being discharge destination (home, rehabilitation, or nursing home), and the predictors being Mobility Scale for Acute Stroke Score, MBI continence scores, age, and social situation. The overall amount of variability explained in discharge destination by the predictors was 63% (Nagelkerke pseudo R²). The regression analysis was repeated, adding unit code as a predictor. Unit code was a significant contributor to the model (P<.01).
The results of the study indicate that, after adjusting for case mix, there may be variations in practice in selection for rehabilitation leading to inequities of access.
Disease classification systems in rehabilitation for out-patients
2006, Rehabilitacion
El objetivo de este trabajo fue elaborar un sistema de clasificación de pacientes en rehabilitación ambulatoria, común para Atención Primaria (AP) y Atención Especializada (AE).
La elaboración siguió dos etapas:Estudio descriptivo de las patologías atendidas a nivel ambulatorio durante el año 2002 en los Centros de Especialidades y un Centro Hospitalario y en las Unidades de Fisioterapia de AP del área sanitaria 1 de la Comunidad de Madrid. Se revisó una muestra aleatoria de 945 historias clínicas de un total de 47.204.Revisión bibliográfica de publicaciones en MEDLINE de sistemas de clasificación de patologías en rehabilitación. Las patologías atendidas en AE se clasificaron en función del diagnóstico principal y en AP en función del motivo de derivación de la consulta del médico de familia a la Unidad de Fisioterapia. Se reagruparon en función de la prevalencia y se codificaron siguiendo los criterios de la Clasificación Internacional de Enfermedades, 9.ª revisión Modificación Clínica (CIE-9-MC) y criterios de la Clasificación Internacional de Atención Primaria (CIAP). Se constituyó un grupo de trabajo con profesionales que participan en el proceso de rehabilitación ambulatoria en el área sanitaria 1 de la Comunidad de Madrid, para consensuar el sistema de clasificación de patologías.
Elaboración de un sistema de clasificación de patologías en pacientes ambulatorios en rehabilitación, común para AP y AE.
Establecer un único sistema de clasificación de pacientes en el proceso de rehabilitación ambulatoria permite describir el tipo de pacientes que demanda atención y comparar las patologías atendidas en los diferentes centros de los dos niveles asistenciales.
The objective of the study has been to develop a classification system of patients for use in out-patient rehabilitation for Primary Care (PC) and Specialized Care (SC).
It was developed in two phases:Descriptive study of diseases attended in out-patient care during the year 2002 in the speciality centers, hospital center and physiotherapy units of health district 1 primary care of the Community of Madrid. A random sample of 945 clinical records out of a total of 47204 were reviewed.Review of the available literature in MEDLINE in order to find patient classification systems in rehabilitation and to analyze its possible application in our setting. The classification was performed using the principal diagnosis of the patient according to International Classification of Diseases 9^th revision Clinical Modification (ICD-9-MC) for patients treated in speciality centers and hospitals centers or International Classification of Primary Care (ICPC) for patients treated in primary care. Secondly, the diseases were grouped based on the prevalence. A workgroup reviewed the patients classification system. The workgroup was constituted by health professionals involved in the process of rehabilitation in out-patient care in health district 1 of the Community of Madrid.
Development of a patients classification system in out-patient rehabilitation, common for PC and SC.
To establish an unique patients classification system in the process of out-patient rehabilitation makes it possible to describe the type of patients that demands health services and to compare the diseases attended in different centers.
Measuring medical complexity during inpatient rehabilitation after traumatic brain injury
2005, Archives of Physical Medicine and Rehabilitation
Ryser DK, Egger MJ, Horn SD, Handrahan D, Gandhi P, Bigler ED. Measuring medical complexity during inpatient rehabilitation after traumatic brain injury. Arch Phys Med Rehabil 2005;86:1108–17.
To compare the performance of 3 severity of illness (SOI) indices—the Comprehensive Severity Index (CSI), All Patient Refined Diagnosis Related Groups Severity of Illness, case-mix group (CMG)—and 5 well-known neurologic parameters, as measures of medical complexity.
Retrospective chart review.
Inpatient rehabilitation center within a level I trauma center.
Consecutive traumatic brain injury (TBI) admissions (N=212).
Acute inpatient TBI rehabilitation. CSI and neurologic parameters were scored by chart extraction. SOI was based on diagnosis codes by using 3M PC Grouper software, version 15.
Adjusted R² was used to predict rehabilitation charges as a proxy of medical complexity.
The highest adjusted R² values for single variables predicting charges were: CMG .349, CSI .293, duration of posttraumatic amnesia .260. Adjusted R² values for the CMG combined with the CSI, 5 neurologic parameters, and SOI to predict charges were .446, .431, and .365, respectively.
The CMG was the best single predictor of rehabilitation charges for TBI. Predictive ability was better when the CMG was combined with the CSI or a combination of the 5 neurologic parameters. A severity index based on objective clinical findings rather than diagnostic codes may have distinct advantages for rehabilitation outcome studies and reimbursement methodology.
Thesaurus type tools as coding aid in rehabilitation. Construction concepts and methodology
2005, Rehabilitacion
El tesauro RHBH_RT-2000 es propuesto como una herramienta de ayuda a la codificación, al tener la capacidad de reconducir el lenguaje “hablado” por los profesionales hacia el “lenguaje normalizado” necesario para la codificación diagnóstica.
Se desarrolló en tres fases. Inicialmente se elaboró de un tesauro manual en soporte impreso; en un segundo paso, se realizó la introducción de los mismos en un soporte informático, y, finalmente, durante la fase de aplicación se mantuvo un procedimiento de mejora, mediante la depuración de errores y la introducción de sinónimos y cuasinónimos no incluidos previamente.
Construcción de un tesauro conteniendo 1.800 descriptores, 1.400 sinónimos o cuasinónimos. Se incluyen también un total de 1.425 códigos CIE-9, 127 códigos de síndromes de deterioro y 148 códigos de procedimientos.
Se propone el tesauro como herramienta útil para la codificación no sólo por facilitar la búsqueda de códigos, sino también por permitir establecer relaciones entre campos distintos del conocimiento de modo controlado. No obstante, reconocemos que la búsqueda de soluciones para estos propósitos apartó el instrumento finalmente desarrollado de un tesauro en el sentido ortodoxo, lo que nos hace introducir el concepto herramienta “tipo tesauro”.
RHBH_RT-2000 thesaurus is proposed as a coding help tool, as it has the capacity to redirect the language “spoken” by the professions towards “normalized language” necessary for diagnostic coding.
It was developed in three phases. Initially, it was elaborated from a manual thesaurus in printed form. In a second step, they were introduced into a computer form. Finally, during the application phase, an improvement procedure was maintained by treatment of errors and introduction of synonyms or quasisynonyms not included previously.
Construction of a thesaurus containing 1800 descriptors, 1400 synonyms or quasisynonyms. A total of 1425 ICD-9 codes, 127 codes of deterioration syndromes and 148 codes of procedures were also included.
The thesaurus is proposed as a useful tool for coding not only because it facilitates the search for codes but also because it makes it possible to establish relationships between different fields of knowledge in a controlled way. However, we recognize that the search for solutions for these purposes moves the instrument finally developed away from a thesaurus in the orthodox sense, which is why we have introduced the concept “thesaurus type” tool.
Predicting follow-up living setting in patients with stroke
2002, Archives of Physical Medicine and Rehabilitation
Citation Excerpt :
A summary of the national data set for the United States is published annually and provides benchmarks for functional daily living skills and related information, such as LOS, for a variety of conditions important to interdisciplinary rehabilitation.4,12 In recent years, the information in the UDSMR has been used to predict LOS and develop the basis for a case-mix adjusted prospective payment system in medical rehabilitation.16,17 The system has been extensively reviewed by the Health Care Financing Administration (HCFA, now Centers for Medicare and Medicaid Services) and is referred to as FRGs.18,19
Smith PM, Ottenbacher KJ, Cranley M, Dittmar SS, Illig SB, Granger CV. Predicting follow-up living setting in patients with stroke. Arch Phys Med Rehabil 2002;83:764-70. Objective: To examine living setting at 3- to 6-month follow-up for inpatients with functional impairments discharged from medical rehabilitation. Design: Retrospective performed by using information from the Uniform Data System for Medical Rehabilitation (UDSMR) representing medical rehabilitation patients across the United States. Setting: National survey data. Participants: Information submitted in 1997 and 1998 to the UDSMR by 167 facilities from 40 states was examined. A total of 9587 patient records were included in the final sample. The mean age ± standard deviation was 70.2±12.4 years. The sample included 51.6% women and was comprised of 77.5% non-Hispanic white patients, with an average length of stay of 22.3±4.6 days. Interventions: Not applicable. Main Outcome Measure: Living setting (home vs not at home) at 3- to 6-month follow-up. Results: A discriminant function training model including 8 statistically significant variables correctly classified 85.1% of the patients (n=8149). The total FIM™ instrument score, patient age, function-related group, and marital status were found to be useful classification variables. Wilks λ for the model was.924 (X²=1031.49, P<.000). The area under the receiver operating characteristics curve was.85. Conclusion: The association among functional abilities, demographic characteristics, and follow-up living setting in patients with stroke is complex. Functional variables can be used to help predict follow-up living setting. These variables change based on patient severity level. © 2002 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation

View all citing articles on Scopus

: Supported in part by the National Institutes of Health, grant K08-AG00487 from the National Institute on Aging and grant RO1 HS07595 from the Agency for Health Care Policy and Research. The opinions and conclusions of the author are not necessarily those of the sponsoring agencies.

: No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the authors or upon any organization with which the authors are associated.

View full text

Review articleCase-mix measurement in medical rehabilitation

Abstract

Arch Phys Med Rehabil

Arch Phys Med Rehabil

Arch Phys Med Rehabil

DRGs: their design and development

A psychiatric patient classification system: an alternative to diagnosis-related groups

Med Care

Case mix definition by diagnosis related groups

Med Care

Resource Utilization Groups

A patient classification system for long-term care

Med Care

Refining a case-mix measure for nursing homes: Resource Utilizations Groups (RUG-III)

Med Care

Development of a resource-based classification scheme for rehabilitation

Inquiry

A prototype classification system for medical rehabilitation

A case-mix classification system for medical rehabilitation

Med Care

Federal Register

Clinical Modification

Federal Register

Alternative Systems for Case Mix Classification in Health Care Financing

Predicting charges for inpatient medical rehabilitation using severity, DRG, age, and function

Am J Public Health

Total charges for inpatient medical rehabilitation

Health Care Financing Rev

Predicting inpatient rehabilitation length of stay

Arch Phys Med Rehabil

Charges and Outcomes for Rehabilitation Care

Cost-effectiveness of the Medicare three-hour regulation

Arch Phys Med Rehabil

Review article
Case-mix measurement in medical rehabilitation