Product definition for healthcare contracting: an overview of approaches to measuring hospital output with reference to the UK internal market.

OBJECTIVE--In many industrialised countries, health care third party payers are moving towards contracted provision arrangements with suppliers of hospital care. Essential to such a process is a standard approach to quantifying the care provided. This paper aims to outline the possible approaches to hospital product definition for the UK National Health Service, and recommends appropriate further research. METHODS--All published and unpublished studies on hospital output measurement in the NHS since 1980 were sought for the purposes of the review. This included both discursive and empirical work, and no exclusion criteria were applied. Most empirical reports on this topic, however, come from the United States. Consequently, the published reports since 1980 from the USA, accessed from the Medline and Healthplan CD-ROM databases, have also been included in the overview. CONCLUSIONS--Where data are sufficient, the true casemix approach offers advantages over other methods of output measurement. In the UK NHS, two systems--diagnosis-related groups (DRGs) and healthcare resource groups (HRGs)--are the only casemix measures that have achieved any significant degree of attention. DRGs have been extensively evaluated internationally, and explain variations in resource use in the UK slightly better than do HRGs. As a local product, HRGs can be more easily adapted to the specific needs of the NHS internal market, however, and will thus probably emerge as a better measure for the UK in the long term. In both cases, locally derived cost weights are unavailable, and their development constitutes a major requirement for use in contracting. Adaptations for long stay and outpatient hospital episodes would enhance the usefulness of hospital casemix systems in the NHS. Existing approaches, such as specialty based classifications, are neither standardised nor predictive of resource use, and would be better replaced by casemix systems. Other countries facing similar choices between casemix measurement approaches will need to consider the "trade off" between the adaptability of locally derived systems on the one hand and the low cost, rapidly accessible results, and availability of international comparative data of an imported approach on the other.     

Contributions of case mix and intensity change to hospital cost increases

The 28-percent change in average Medicare inpatient cost per case between 1984 and 1987 is decomposed into three components: input price inflation, changes in average cost within diagnosis-related groups (DRGs) (intensity), and changes in the distribution of cases across DRGs (case mix). We estimate the contributions of technology diffusion and outpatient shifts to within-DRG and across-DRG cost changes. We also use California data to estimate the contribution of changes in the quantity of services provided during a stay. The factors examined account for approximately 80 percent of the real increase in average cost per case.     

Describing Iranian hospital activity using Australian Refined DRGs: a case study of the Iranian Social Security Organisation

OBJECTIVE: To describe Iran's hospital activity with Australian Refined Diagnosis Related Groups (AR-DRGs). METHOD: A total of 445,324 separations was grouped into discreet DRG classes using AR-DRGs. L(3)H(3); IQR and 10th-95th percentile were used to exclude outlier cases. Reduction in variance (R(2)) and coefficient of variation (CV) were applied to measure model fit and within group homogeneity. RESULTS: Total hospital acute inpatients were grouped into 579 DRG groups in which 'surgical' cases represented 63% of the total separations and 40% of total DRGs. Approximately 12.5% of the total separations fell into DRGs O60C (vaginal delivery) and 28% of the total separations classified into major diagnostic category (MDC) 14 (pregnancy and childbirth). Although reduction in variance (R(2)) for untrimmed data was low (R(2)=0.17) for LOS, trimming by L(3)H(3), IQR, and 10th-95th percentile methods improved the value of R(2) to 0.53, 0.48, and 0.51, respectively. Low value of R(2) for AR-DRGs within several MDCs were identified, and found to reflect high variability in one or two DRGs. High within-DRG variation was identified for 23% of DRGs using untrimmed data. CONCLUSION: Low quality and incomplete data undermines the accuracy of casemix information. This may require improvement in coding quality or further classification refinement in Iran. Further study is also required to compare AR-DRG performance with other versions of DRGs and to determine whether the low value of R(2) for several MDCs is due to the weakness of the AR-DRG algorithm or to Iranian specific factors.     

浙江省住院病人病例组合研究

目的建立基于浙江省医院病案首页信息且覆盖常见病种的疾病诊断相关组（diagnosisrelated groups，DRGs）。方法应用自动交互检测方法（automatic interaction detector，AID），对浙江省住院病人进行病例组合研究，用RIV（reduction in variance）值、变异系数、非参数检验和回代检验对建立的方案进行评价。结果根据ICD-10建立19个主要诊断类目及53个细类，分析后共得266个病例组合。RIV值平均值为0．2573，变异系数平均值为87．70％，所有组合经Kruskal—Wallis检验，P值均〈0．0001，且回代检验效果较好，均说明病例组合方案合理。结论应用AID方法研究病例组合，对浙江省住院病人建立覆盖常见病种的包含266个组合的DRGs是合理的。     

Inpatient resource use: a comparison of family medicine and internal medicine physicians.

BACKGROUND. It is not known whether differences exist between the use of inpatient resources by family medicine and internal medicine physicians when patient demographic and complexity variables are statistically controlled. METHODS. The study population was all patients in 13 higher volume diagnosis-related groups (DRGs) discharged from the family medicine (n = 306) and internal medicine services (n = 2374) of the University of Cincinnati Hospital during 1985 and 1986. The dependent variables were length of stay and inpatient readmission within 2 weeks. Stratification by DRGs was used to control for the effects of age and case mix on these variables. RESULTS. With the exception of findings regarding one DRG, the results do not indicate that differences exist in average length of stay between patients of family medicine and internal medicine physicians after adjustment for other variables. Furthermore, almost all of the explained variance in length of stay was attributed to patient complexity and not to physician specialty or patient race or sex. For all discharges, the proportion of patients readmitted within 2 weeks was about 4% higher for the internal medicine service. However, multivariate analysis did not support the importance of physician specialty (family medicine or internal medicine) as a predictor of whether readmission occurred within 2 weeks. CONCLUSIONS. General indicators of resource use (such as length of stay or readmission occurrence) without adjustment for patient case mix, demographics, and complexity are inadequate for comparison of health care providers. Further research regarding interspecialty differences should use longitudinal data from large populations, which would permit more detailed examination of resource utilization.     

Striving towards efficiency in the Greek hospitals by reviewing case mix classifications

In order to verify the efficiency level of Greek public hospitals, this paper evaluates the most recent indicators. Relevant data were collected from the two following databases: (a) hospitals' utilisation data generally and per clinical speciality [Ministry of Health, Athens, (Data based) 1995]; (b) Patients' and hospitals' characteristics per diagnosis [National Statistical Office, Athens, (Data based) 1993]. As explanatory variables, the study examines supply and demand factors following case mix classifications. Firstly, average length of stay (ALOS) and secondly, cost per case were regressed as dependent variables. The study highlights the extent of variability across hospitals for different groups of patients with the same condition. The results specify the most important factors that affect ALOS and cost pertaining to efficiency. Per speciality analysis shows occupancy, size-type of the hospital, beds and doctors per speciality, access and use of outpatient services, and surgical operations, etc. as the most significant factors. Per disease-diagnosis analysis shows age of over 65 years, gender, residence, marital status, surgical operation and insurance as the most important factors. General cost analysis in all National Health Systems (NHS) hospitals shows that economies of scale appear in: (a) district and/or specialised hospitals of 250-400 beds; (b) regional and/or teaching hospitals of over but near to 400 beds. Consequently, the author determines the 'Greek' Diagnostic Related Groups (DRGs), based on the cost per clinical speciality in the nine basic specialities and on the cost per diagnosis of the top 15 diagnoses. Further to the scientific results, such studies will enhance much necessary discussions on the organisation of service delivery and financing, by following case mix classification.     

云南省综合性医院住院病人DRGs组合方式研究

在参阅文献的基础上，采用树型模型-AID法，对云南省5所综合性医院的395 307份住院病例进行了病例组合，总结出云南省DRGs病例组合方法，为合理测量医疗产出、指导医院制订合理收费标准、有效控制医疗费用上涨等提供了科学依据。     

Measuring outpatient resource use and case mix in ophthalmology in north east England

OBJECTIVES: To test the extent to which two existing ambulatory case mix measures (Ambulatory Visit Groups and Ambulatory Patient Groups) and other variables can explain resource use variations in ophthalmic outpatient visits. DESIGN: Three week prospective study of three consultant outpatient clinics. SETTING: One ophthalmic hospital (Sunderland Eye Infirmary, Sunderland, Tyne and Wear) and three outreach clinics (South Tyneside District Hospital, South Shields, Tyne and Wear; Dryburn Hospital, Durham, Co Durham; and Hartlepool General Hospital, Hartlepool, Cleveland). SUBJECTS: 325 patients who visited ophthalmic outpatient clinics. MAIN OUTCOME MEASURES: Mean consultation time and mean cost distributions by case mix group, analysed by analysis of variance. RESULTS: Ambulatory case mix measures can explain some of the variation in resource use for outpatient visits, but different measures differ in the extent to which they can do so. Clinicians' behaviour also accounts for a significant amount of such variation. Simpler measures of visit type, without diagnostic or procedure information, do not explain resource use variations. CONCLUSIONS: Existing measures perform reasonably well, but their data requirements may preclude their introduction in the National Health Service. Caution is required in advocating simpler measures, however. The influence of clinical practice on resource use variations is important; in this study, most differences between clinicians were not attributable to differences in case mix.

 

     

Case-mix adjustment using objective measures of severity: the case for laboratory data.

OBJECTIVE. We evaluate the use of routinely gathered laboratory data to subclassify surgical and nonsurgical major diagnostic categories into groups homogeneous with respect to length of stay (LOS). DATA SOURCES AND STUDY SETTING. The source of data is the Combined Patient Experience database (COPE), created by merging data from computerized sources at the University of California San Francisco (UCSF) Medical Center and Stanford University Medical Center for a total sample size of 73,117 patient admissions. STUDY DESIGN. The study is cross-sectional and retrospective. All data were extracted from COPE consecutive admissions; the unit of analysis is an admission. The outcome variable LOS proxies hospital resource utilization for an inpatient stay. Nine (candidate) predictor variables were derived from seven lab tests (WBC, Na, K, C02, BUN, ALB, HCT) by recording the whole-stay minimum or maximum test result. DATA COLLECTION/EXTRACTION METHODS. Patient groups were formed by first assigning to major diagnostic categories (MDCs) all 73,117 admissions. Each MDC was then partitioned into medical and surgical subgroups (sub-MDCs). The 13 sub-MDCs selected for study define a study population of 32,599 patients that represents approximately 45 percent of inpatients. Within each of the 13 sub-MDCs, patients were randomly assigned to one of two data sets in a ratio of 2:1. The first set was used to create, the second to validate, three different LOS predictors. Predictive accuracies of individual DRG classes were compared with those of two alternative classification schemes, one formed by recursive partitioning (the sub-MDC) using only lab test results, the other by partitioning with both lab test results and individual DRGs. PRINCIPAL FINDINGS. For the eight largest sub-MDCs (81 percent of study population), individual DRGs explained 23 percent of the within sub-MDC variance in LOS, laboratory data classes explained 31 percent, and classes derived by considering individual DRGs and laboratory data explained 37 percent. (Each result is a weighted average R2. The average number of LOS classes into which the eight largest sub-MDCs were partitioned were 20, 10, and 10, respectively. Within six of the eight, partitioning on the basis of laboratory data alone explained more within sub-MDC variance than did partitioning into individual DRGs. CONCLUSIONS. Routine lab test data improve the accuracy of LOS prediction over that possible using DRG classes. We note that the improvements do not result from overfitting the data, since the numbers of LOS classes we use to predict LOS are considerably fewer than the numbers of individual DRGs.     

Chronic comorbidity and homogeneity in diagnostic related groups]

OBJETIVE: One of the ways to compare the efficiency of different hospitals and services is to evaluate Diagnostic Related Groups (DRGs), with the hypothesis that patients in the same RDG will present homogeneous behavior with respect to length of stay. The object of this study was to evaluate in the context os the National Health System the internal variability of specific DRGs in terms of the patients' comorbidity. METHODS: On the basis of various comorbidity scores measured with the Charlson index (ChI), we analyzed length of stay, inhospital mortality and emergency readmissions at 30 and 365 days in 106.673 hospitalizations (excluding subjects younger than 17 years of age, and obstetrics and psychiatric patients) in 12 hospitals, and in 17 DRGs selected on the basis of their greater frequency and comorbidity. RESULTS: In the aggregated analysis, length of stay (from 8.5 days in patients with no comorbidity to 17.0 days in patients with scores higher than 4) and inhospital mortality rates (from 3.7% in patients with no comorbidity to 17.6% in patients with highest score) increased significantly with each level of the Charlson index. The readmission rate at 30 days rose from 4.7% to 10.9% also in step with increases in comorbidity scores. Readmissions at one year varied from 14.8% in patients with scores of 0 to 35.2% in patients with scores of 3-4, and dropped to 27.9% in patients with scores higher than 4. When analysing different DRGs, 8 of the 17 groups studied showed a significantly higher length of stay with increased comorbidity scores. Some DRGs also showed intra-group variability with respect to mortality and readmission, particularly at 365 days. CONCLUSIONS: Some DRGs show significant internal variability in terms of comorbidity that may be generating a false worse evaluation of the efficiency of hospitals that treat patients with higher comorbidity.     

Variation in Hospital Length of Stay: Do Physicians Adapt Their Length of Stay Decisions to What Is Usual in the Hospital Where They Work?

OBJECTIVE: To test the hypothesis that physicians who work in different hospitals adapt their length of stay decisions to what is usual in the hospital under consideration. DATA SOURCES: Secondary data were used, originating from the Statewide Planning and Research Cooperative System (SPARCS). SPARCS is a major management tool for assisting hospitals, agencies, and health care organizations with decision making in relation to financial planning and monitoring of inpatient and ambulatory surgery services and costs in New York state. STUDY DESIGN: Data on length of stay for surgical interventions and medical conditions (a total of seven diagnosis-related groups [DRGs]) were studied, to find out whether there is more variation between than within hospitals. Data (1999, 2000, and 2001) from all hospitals in New York state were used. The study examined physicians practicing in one hospital and physicians practicing in more than one hospital, to determine whether average length of stay differs according to the hospital of practice. Multilevel models were used to determine variation between and within hospitals. A t-test was used to test whether length of stay for patients of each multihospital physician differed from the average length of stay in each of the two hospitals. PRINCIPAL FINDINGS: There is significantly (p<.05) more variation between than within hospitals in most of the study populations. Physicians working in two hospitals had patient lengths of stay comparable with the usual practice in the hospital where the procedure was performed. The proportion of physicians working in one hospital did not have a consistent effect for all DRGs on the variation within hospitals. CONCLUSION: Physicians adapt to their colleagues or to the managerial demands of the particular hospital in which they work. The hospital and broader work environment should be taken into account when developing effective interventions to reduce variation in medical practice.     

DRG-Based Cost Minimization Models: Applications in a Hospital Environment

The primary objective of this article is to investigate the feasibility of the application of cost minimization analysis in a teaching hospital environment. The investigation is concerned with the development of cost per admission and cost per patient day models. These models are further used for determining the value of the length of stay that would minimize cost per patient day (projected length of stay) and for estimating the costs. This study is based on total of 94,500 observations (1999 and 2000), obtained from a teaching hospital in South Florida. The top ten Diagnosis Related Groups (DRGs) with the highest volume are selected and classified into four insurance categories: Medicaid, Medicare, commercial, and self-pay. The cost models are fitted to the data for an average R2 value of 79%, and a MAPE value of 15%. The result demonstrates that if a hospital can control the length of stay at the projected level, on average, the cost per admission and the cost per patient day will decrease. Based on 6,367 admissions for the selected DRGs in 2000, the total cost per year and the cost per patient day decreased by approximately 11.58 and 10.35%, respectively. Overall, these results confirm that the concept of cost minimization analysis in economic theory can be applied to healthcare industries for the purpose of reducing of costs. In addition, this research offers a decision support instrument for healthcare administrators.     

Geographic variation in the use of post-acute care

OBJECTIVE: To assess the extent and consistency of geographic differences in the use of post-acute care (PAC), and the stability of this pattern of variation. DATA SOURCES: The 5 percent Medicare data sample for 1996, 1997, and the first eight months of 1998 were used. STUDY DESIGN: Patterns of PAC use for various Diagnosis-related Groups (DRGs) cross states (33 with enough cases per year) and census divisions were examined. The consistency of relative rankings for overall PAC use and use within defined DRGs was compared. PRINCIPAL FINDINGS: PAC use varied substantially across regions. For example, the extent of any PAC use for stroke patients varied by 12 percentage points among census regions in 1998. The pattern of PAC use was quite consistent across years; 30 of the 36 possible Spearman rank order correlations were statistically significant with coefficients ranging from 0.35 to 0.95 among the DRGs studied. The correlations among DRGs were generally high. For skilled nursing facility use, all the correlations were above 0.5 and were statistically significant; in general the patterns were highest within medical DRGs (0.65-0.93). CONCLUSIONS: The variation in PAC use is not a statistical artifact. It is likely the result of several forces: practice styles, supply of services, and local regulatory practices.     

Out-patient case mix: a survey of user requirements

Research and development of out-patient case mix systems, to plan and monitor resource use in the out-patient sector, has hitherto not been accorded priority in the NHS. As part of an investigation of their usefulness, a survey of NHS professionals' requirements for out-patient case mix was conducted. The results confirmed that there was support for developing out-patient case mix systems, although different users had different requirements. However, a common theme to emerge was the desirability of constructing holistic systems which cover in-patient, day-case and out-patient care. Additionally, development of care packages was seen as a necessary first step in constructing systems for out-patient care. Concludes that visit-based case mix systems are unlikely to fulfil users' requirements and recommends that case mix contracting projects by Healthcare Resource Groups should be extended to the out-patient sector, but must recognize that existing systems do not meet users' requirements.     

Does managed care make a difference? Physicians' length of stay decisions under managed and non-managed care

Background

In this study we examined the influence of type of insurance and the influence of managed care in particular, on the length of stay decisions physicians make and on variation in medical practice.

Methods

We studied lengths of stay for comparable patients who are insured under managed or non-managed care plans. Seven Diagnosis Related Groups were chosen, two medical (COPD and CHF), one surgical (hip replacement) and four obstetrical (hysterectomy with and without complications and Cesarean section with and without complications). The 1999, 2000 and 2001 - data from hospitals in New York State were used and analyzed with multilevel analysis.

Results

Average length of stay does not differ between managed and non-managed care patients. Less variation was found for managed care patients. In both groups, the variation was smaller for DRGs that are easy to standardize than for other DRGs.

Conclusion

Type of insurance does not affect length of stay. An explanation might be that hospitals have a general policy concerning length of stay, independent of the type of insurance of the patient.     

How well do diagnosis-related groups for appendectomy explain variations in resource use? An analysis of patient-level data from 10 European countries

Appendectomy is a common and relatively simple procedure to remove an inflamed appendix, but the rate of appendectomy varies widely across Europe. This paper investigates factors that explain differences in resource use for appendectomy. We analysed 106,929 appendectomy patients treated in 939 hospitals in 10 European countries. In stage 1, we tested the performance of three models in explaining variation in the (log of) cost of the inpatient stay (seven countries) or length of stay (three countries). The first model used only the diagnosis-related groups (DRGs) to which patients were coded, the second model used a core set of general patient-level and appendectomy-specific variables, and the third model combined both sets of variables. In stage two, we investigated hospital-level variation. In classifying appendectomy patients, most DRG systems take account of complex diagnoses and comorbidities but use different numbers of DRGs (range: 2 to 8). The capacity of DRGs and patient-level variables to explain patient-level cost variation ranges from 34% in Spain to over 60% in England and France. All DRG systems can make better use of administrative data such as the patient's age, diagnoses and procedures, and all countries have outlying hospitals that could improve their management of resources for appendectomy.     

疾病诊断相关分组(DRGs)在肿瘤门诊特殊疾病病种支付中的应用研究

通过多元线性回归模型筛选对医疗费用有统计学意义的影响因素并筛选费用异常数据,利用基于E-CHAID算法的决策树模型进行DRGs分组,用变异系数、方差减少量及非参数检验验证分组的合理性。通过统计模型剔除11条异常数据后共分为7个DRGs组,经CV、RIV及秩和检验验证后证实分组效果合理且较为稳定。相关部门可以门诊特殊疾病为试点逐步推开门诊DRGs的应用,分组时需从数据分布特点入手,在合理的数据基础上结合疾病特征、治疗方式等因素进行分组并动态调整,将DRGs分组的"事前控制"转变为"事前测算—事中控制—事后调整",提高分组的综合性和实用性。     

Primary Care Groups and NHS Rationing: Implications of the Child B Case

Implementing The new NHS and the 1997 NHS (Primary Care) Act will gradually extend cash-limiting into primary health care, especially general practice. UK policy-makers have avoided providing clear, unambivalent direction about how to 'ration' NHS resources. The 'Child B' case became an epitome of public debate about NHS rationing. Among many other decision-making processes which occurred, Cambridge and Huntingdon Health Authority applied an ethical code to this rationing decision. Using new data this paper analyses the rationing criteria NHS managers and clinicians used at local level in the Child B case; and the organisational structures which confronted them with such decisions. Primary Care Groups are likely to confront similar rationing decisions in respect of 'gate-kept' NHS services. However, such rationing processes are not so easily transposed to open-access services such as general practice. NHS rationing decisions, especially in PCGs, will require a much more specific ethical code than hitherto used. This revised version was published online in July 2006 with corrections to the Cover Date.     

A comparison of the resource intensity of inpatients in urban and rural nonteaching hospitals

PL 98-21 mandated a prospective payment system based on diagnosis related groups (DRGs) for all Medicare inpatients. The predetermined payment for each DRG is intended to reflect the resources used to treat patients within the DRG. Eventually, the system will allow for one payment level for each DRG in rural hospitals and a higher payment level for the same DRG in urban hospitals. This represents an equitable approach, provided there is not a predominance of high severity cases in rural hospitals and that higher costs in urban hospitals are reflective of higher priced exogenous factors beyond the control of the hospital. Equitability also requires that DRGs capture the resource intensity of treatment for a given classification of patients, equally for urban and rural patients. This work compares the pediatric population of urban hospitals without a pediatric residency program with that of rural hospitals in terms of major diagnostic category, DRG, disease severity, length of stay, and charges. It also compares the capacity of DRGs to explain the variation in resource consumption in urban and rural hospitals. A sample of 116,721 discharges from 130 urban hospitals and a sample of 54,073 discharges from 97 rural hospitals are used in this work. The results indicate that there is no difference in the patient populations of these two hospital groups. The results also indicate that DRGs explain only 50 percent of the variance in the resource variables, but this obtains equally for both populations.