Case mix analysis is the study of types of patients treated by a facility


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The facility is reimbursed a predetermined amount for each patient episode. Casemix systems and in particular DRG systems mean that a lump sum is paid based on a diagnosis and maybe particular treatment aspects. Comorbidity may trigger an increase in the sum paid. Such systems tend to set the incentive to provide treatment at the lowest possible costs, and to have many treatment cases in order to improve the revenues of the health provider. Also expensive cases that could have complications are avoided, and side diagnose left for treatment in separate stays. Further, there is an ongoing struggle between those using the comorbidity of increase the price in individual cases, and those who calculate the price that is going to be paid for a particular case group in the following year.

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Casemix systems are liked by economists because they may effectively reduce the costs of treatment. However, in order to ensure that the quality of treatment does not suffer from attempts by service providers to reduce the costs of cases, extensive monitoring of outcome quality is essential. Casemix systems come with costs for administration for the quality monitoring systems, for the cost calculating institutes, and for keeping diagnosis and procedure coding schemes up to date.

Unfortunately these costs are never mentioned in the reports. A side effect of casemix is also the shortening of the stay of patients and some increased time for administrative work in hospitals. As such, the so beneficial time between health providers and patients is considerably reduced. Also, the focus of attention shifts from a holistic view of a human patient to 'a disease that is treated'.

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Physicians who meet both quality and cost performance measures receive 2 stars. Those who meet only quality measures receive 1 star. Cost performance is assessed by comparing the percentile rankings of the physician episode costs with a peer group within the same geographic area and specialty. In order to meet the quality criteria, physicians must perform at a level that meets or exceeds the 75th percentile performance for all physicians measured. Inpatient procedures are risk-adjusted by 3M CRGs severity of illness level. Cost performance analysis is based on total cost—a combination of resource utilization, resource mix, and unit cost—for an episode of care.

Episodes include dollars paid to the physician for direct services as well as facility costs and ancillary services that the software logic determined were related eg, medications and diagnostic tests. The sets of comparable episodes for all peer group physicians are combined and ranked from lowest to highest percentile. UnitedHealthcare disseminates performance information directly to consumers online and via e-mail and print materials.

Clinical performance is evaluated on the basis of hospital readmission rates after 30 days; rates of complications during hospital care; and other treatments, by specialty, shown to improve outcomes. IPPIP goals are the following:. Half of the award is based on medical cost management measured via an annual risk-adjusted, per-member, per-month cost target.

The other half is based on achieving quality indicators.

The quality standards a provider must meet to earn a full reward are as follows:. These weights reflect the illness burden. This example is medical only. Some models also have a pharmacy component. A risk score of 1. We can normalize scores and reference them to our own population, Population A, whose average risk is 1.

John Smith, a member of Population A, has a relative risk score of 5.

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John Smith is 4. We can convert relative risk scores to dollars by multiplying the scores by the mean expenditures of the benchmark population. There are many different kinds of DxCG models, many that take medical and medications into account. The models can be used for medical management disease management , for analytics output can be factors going into other in-house predictive models , and to adjust for provider incentive programs.

To address rising health care costs, payers and employers have developed a number of initiatives, including pay-for-performance and value-based tier products, designed to steer patients toward preferred providers. The assumption behind these initiatives is that robust performance measures allow purchasers of care to identify the most efficient providers. Health care efficiency is the cost of care associated with a specific level of quality AQA Quality measures are now well established, but measures of health care efficiency are not.

Most studies of hospital performance and most episode groupers used to attribute costs and utilization of resources to individual physicians do not identify the relationship between quality and cost of care. Therefore, current efficiency measures analyze economic performance and provide cost profiling without adjusting for quality. Aside from health care efficiency, the reliability of conventional cost profiling of individual physicians is questionable.

While the validity of different methods of assigning episodes to physicians is established, we cannot be sure they accurately assign the portion of cost variability for which the individual physician is truly responsible. The authors used commercial software to construct episodes of care from claims data provided by 4 health plans in Massachusetts. Reliability varied by specialty, ranging from 0.

The authors concluded that current methods of cost performance may produce misleading results. Recently, Timbie and colleagues suggested that comparing cost performance of individual providers with the average costs of the entire peer group may be the primary reason for the low reliability demonstrated by conventional cost-profiling methods, particularly when applied to specialties Timbie Instead, the authors have proposed the use of propensity score weighting, which adjusts for variables covariates such as practice size and types of services rendered.

In their study, each physician was compared with a subset of his or her peer group with a similar episode mix instead of the entire specialty group.

Casemix funding for acute hospital inpatient services in Australia

Then, cost performance calculated with the propensity score weighting was compared against that obtained with conventional groupers tools entire specialty used as the peer group. Current SFA methods calculate hospital efficiency by adjusting economic performance for burden of illness and quality of care. However, SFA methods are mostly confined to research.

With respect to risk measures used in conjunction with episode groupers, their proprietary ownership has prevented close scrutiny. Several factors limit the accuracy of risk adjustment models, such as inadequate data, patient socioeconomic status, and even patient preferences. In addition, not all severity measures perform best across all conditions. Nevertheless, Iezzoni has shown that the best approach to risk adjustment is to use specific models shown to perform well for the specific outcomes of interest Iezzoni No matter how imperfect, risk adjustment must be part of the health care efficiency equation that includes cost and quality because it improves our understanding of what portion of the variability in cost and quality should be attributed to intrinsic patient factors rather than provider performance.

While the IOM considers efficiency as a characteristic of high-quality care, currently a single measure of the cost of care associated with a specific level of quality of care is not commercially available. In addition, cost-of-care profiles have little correlation with quality measures Rattray Further research is needed for measurement of health care efficiency to advance and become accepted.


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To be useful, health care efficiency measures need to assess resource use accurately as an input and health outcomes as an output and account for the variability in the costs of producing high-quality care. Accessed June 11, Physician cost profiling—reliability and risk of misclassification. N Engl J Med. Under the PPS, the payment-per-dialysis treatment has been expanded to include services that previously were reimbursed separately, and payments are now case-mix adjusted to account for the variable costs of care for patients of different ages and body size and for the presence of certain co-morbidities.

The ESRD PPS as implemented in used patient case-mix from the —08 Medicare fee-for-service population to set weights for each patient-level case-mix adjuster in the payment formula. The prevalence of each case-mix adjuster underlying the payment weights instituted for the PPS was not made public, either in the Proposed Rule issued in September , 2 or in the Final Rule, 1 and concerns were raised by several provider organizations that the assumed prevalence, especially for chronic and acute co-morbidities, was higher than dialysis facilities would be able to report for billing purposes.

Further, little is known about how stable the prevalence of these case-mix adjusters has been over time, either before the period used to set payment weights or since. An analysis of data found in the ADR 8 showing year trends in the prevalence of patients by age cohort and the percent of patients new to dialysis illustrates that the percentage of patients in these case-mix adjuster categories has indeed changed over time.

For example, the percent of patients in the high-payment multiplier age cohort declined by Similarly, the highest payment multiplier is applied to patients new to dialysis incident , a category that had a While CMS has updated some components of the PPS since implementation in , including annual adjustments to the outlier formula, a recalibration of the base rate to account for decreased drug utilization in and modifications to the ESRD market basket and the wage portion of the base rate for , no updates have been made to the patient case-mix payment adjusters.

CMS has indicated that it will conduct this analysis as part of the rule-making process. Therefore, given the trends observed in the USRDS data, the lack of information about the prevalence of the case-mix adjusters used for payments, and the widespread recognition of the need for monitoring and analysis of the PPS case-mix adjusters, a timely review is needed. As part of this process, this study was designed to analyze the changes in patient-level case-mix adjuster prevalence in the Medicare Fee-For-Service FFS population in the years leading up to the PPS implementation — The primary goal of the study was to provide population-level estimates of, and trends in, prevalence of case-mix factors that were to be applied to payments under the ESRD PPS, up to the time period used to set the PPS adjusters.

It was hoped that such an analysis would lend insights into what the baseline prevalence of each case-mix adjuster used by the PPS payment formula actually was, and how case-mix prevalence in this population has changed over time. Data were split into 3-year cohorts: —, —, and — The prevalence of a PPS patient-level case-mix adjuster was counted for those calendar months in which the patient was on dialysis and covered by Medicare as the primary payer for more than half of the month.

Note that the day waiting period for a new dialysis patient was not included. Thus, the first 3 months for a patient who began dialysis but was not already covered by Medicare were not included in this analysis. The percentage of all patients and of all patient months within each 3-year cohort was calculated for each case-mix adjuster.

Percent change in case-mix adjuster prevalence was then calculated between 3-year cohorts. No statistical significance tests were performed on the differences in case-mix prevalence between the 3-year cohorts because:. No statistical inferences were made for any other population. Case-mix prevalence calculations We conducted two separate analyses based on the following prevalence definitions:.

Percentage of patients: The denominator is the number of patients who received dialysis and were covered by Medicare for at least one month in a given 3-year cohort. Case-mix adjuster definitions replicated the PPS reimbursement definitions as closely as possible.

Age was defined at first date of ESRD only. For patient prevalence, age was reported for the median month of dialysis treatment during each cohort period.

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Functional mapping of hospitals by diagnosis-dominant case-mix analysis

For example, a patient treated with dialysis from Feb to June 17 months total had a median month of dialysis treatment of Oct ninth month , and thus the age in was reported. For acute conditions pericarditis, bacterial pneumonia, gastrointestinal bleeding , once a claim was found, a patient was considered to have the acute condition for that month and three subsequent months, per the PPS reimbursement rules. Any claim within these four months was considered to be part of the same acute episode.

For chronic conditions hereditary hemolytic and sickle cell anemia, monoclonal gammopathy, myelodysplastic syndrome , once a claim was found, a patient was considered to have the condition for that and all subsequent months. Prior to , the code Therefore, we included Two methods were used to validate the study results.

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First, prevalence estimates were compared to those for a Medicare patient cohort published in a report to Congress, 17 which used very similar, although not identical, case-mix category definitions to those ultimately promulgated by CMS in the PPS Final Rule and subsequent regulations. Second, an estimated mean patient payment multiplier was calculated for the cohort by running the prevalence estimates through a validated PPS payment multiplier model. Per the Final Rule, the standardization adjustment including all facility and patient factors was.

This standardization adjustment included the estimated effect of the facility low-volume adjuster LVA. In order to estimate the patient-only case-mix multiplier, we estimated the facility-level adjustments for the LVA to be worth 0. Table 1 shows the prevalence of each patient-level case-mix adjuster in each of the three 3-year cohorts, as well as the prevalence reported for in the Leavitt Report. Large percentage changes were observed in the prevalence of many case-mix categories between —02 and — Large declines were observed in prevalence of low BMI patients — There were large changes in patient-level case-mix adjuster prevalence in the Medicare ESRD population between —02 and — Important demographic changes included:.

New dialysis patients as a percent of total declined, with fewer patients in the high payment adjuster age cohort 18—44, and increasing prevalence of chronic comorbidities.

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