John S. Hughes

IDENTIFYING COMPLICATIONS OF CARE USING ADMINISTRATIVE DATA

The Complications Screening Program (CSP) is a method using standard hospital discharge abstract data to identify 27 potentially preventable in-hospital complications, such as post-operative pneumonia, hemorrhage, medication incidents, and wound infection. The CSP was applied to over 1.9 million adult medical/surgical cases using 1988 California discharge abstract data. Cases with complications were significantly older and more likely to die, and they had much higher average total charges and lengths of stay than other cases (P < 0.0001). For most case types, 13 chronic conditions, defined using diagnosis codes, increased the relative risks of having a complication after adjusting for patient age. Cases at larger hospitals and teaching facilities generally had higher complication rates. Logistic regression models to predict complications using demographic, administrative, clinical, and hospital characteristics variables, had modest power (C statistics = 0.64 to 0.70). The CSP requires further evaluation before using it for purposes other than research.

Clinical Risk Groups (CRGs): A classification system for risk-adjusted capitation-based payment and health care management

ObjectiveTo develop Clinical Risk Groups (CRGs), a claims-based classification system for risk adjustment that assigns each individual to a single mutually exclusive risk group based on historical clinical and demographic characteristics to predict future use of healthcare resources. Study Design/Data SourcesWe developed CRGs through a highly iterative process of extensive clinical hypothesis generation followed by evaluation and verification with computerized claims-based databases containing inpatient and ambulatory information from 3 sources: a 5% sample of Medicare enrollees for years 1991–1994, a privately insured population enrolled during the same time period, and a Medicaid population with 2 years of data. ResultsWe created a system of 269 hierarchically ranked, mutually exclusive base-risk groups (Base CRGs) based on the presence of chronic diseases and combinations of chronic diseases. We subdivided Base CRGs by levels of severity of illness to yield a total of 1075 groups. We evaluated the predictive performance of the full CRG model with R2 calculations and obtained values of 11.88 for a Medicare validation data set without adjusting predicted payments for persons who died in the prediction year, and 10.88 with a death adjustment. A concurrent analysis, using diagnostic information from the same year as expenditures, yielded an R2 of 42.75 for 1994. ConclusionCRGs performance is comparable to other risk adjustment systems. CRGs have the potential to provide risk adjustment for capitated payment systems and management systems that support care pathways and case management.

Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method.

OBJECTIVES This research examined whether judgments about a hospitals risk-adjusted mortality performance are affected by the severity-adjustment method. METHODS Data came from 100 acute care hospitals nationwide and 11880 adults admitted in 1991 for acute myocardial infarction. Ten severity measures were used in separate multivariable logistic models predicting in-hospital death. Observed-to-expected death rates and z scores were calculated with each severity measure for each hospital. RESULTS Unadjusted mortality rates for the 100 hospitals ranged from 4.8% to 26.4%. For 32 hospitals, observed mortality rates differed significantly from expected rates for 1 or more, but not for all 10, severity measures. Agreement between pairs of severity measures on whether hospitals were flagged as statistical mortality outliers ranged from fair to good. Severity measures based on medical records frequently disagreed with measures based on discharge abstracts. CONCLUSIONS Although the 10 severity measures agreed about relative hospital performance more often than would be expected by chance, assessments of individual hospital mortality rates varied by different severity-adjustment methods.

How severity measures rate hospitalized patients

or, for A c u t e Phys io logy a n d C h r o n i c H e a l t h II (APACHE II) a n d t h e C o m o r b i d i t y Index , b y ou r se lves . D i s c h a r g e a b s t r a c t s a n d b r i e f n a r r a t i v e s u m m a r i e s for t h e five c a s e s a p p e a r in T a b l e 2. We iden t i f i ed t h e m a j o r d e t e r m i n a n t s o f t h e i nd iv id u a l seve r i ty r a t i n g s p r e s e n t e d in Tab le 3, e i t h e r b a s e d o n t h e c o m m e n t a r y p r o v i d e d b y v e n d o r s o n t h e i r r a t i n g s of t h e ca ses , or b y r ev iewing t h e s c o r i n g logic p r e s e n t e d i n p u b l i s h e d m a t e r i a l s . T h e v e n d o r s of two of t h e c h a r t b a s e d m e a s u r e s , M e d i s G r o u p s a n d CSI, u s e d t h e n a r r a t ive s u m m a r i e s to sco re ca ses . For t h e o t h e r c h a r t b a s e d m e a s u r e , APACHE II, t h e a u t h o r s de r ived s c o r e s f r o m t h e c l in ica l a n d l a b o r a t o r y d a t a i n t h e n a r r a t i v e s u m m a r i e s . We o b t a i n e d r a t i n g s for five of t h e six c o d e b a s e d m e a s u r e s f rom t h e i r v e n d o r s , w h o g e n e r a t e d t h e i r r a t i n g s u s ing on ly d i s c h a r g e a b s t r a c t i n f o r m a t i o n , p r i m a r i l y t h e d i s 308 Hughes et al., Severity Measures and Hospital Patients JGIM Table 3. Summary of Case Scoring for Illustrative Patients Range Case I Case 2 Case 3 Case 4 Case 5 Ordinal scales AIM Score 1-5 4 2 4 3 1 APR-DRGs 1-4 4 4 4 3 2 CSI score Admission 1-4 2 4 1 2 3 Maximum 1-4 2 4 4 4 4 MedisGroups admission severity 0-4 2 2 2 1 3 PMCs Severity Level (SL) 1-7 6 5 5 7 4 Refined DRGs (R-DRGs) D,C,B,A B A B A B Integer scores APACHE II Acute Physiology score 0--60 4 19 5 1 8 Total 0-71 10 26 8 7 10 Comorbidity Index 0-31 0 0 1 0 2 CSI integer score Admission 0+ 25 65 20 29 35 Maximum 0+ 25 65 73 83 80 Mortality prediction Disease Staging mortality 0-1.0 0.217 0.208 0.333 0.284 0.054 MedisGroups probability of death Admission 0-1.0 0.029 0.290 0.200 0.009 0.129 Midstay 0--1.0 0.029 0.009 0.013 0.415 Length of stay AIM LOS prediction (days) >0 8.0 1.9 3.0 15.1 7.3 PMCs LOS prediction (days) >0 12.7 12.1 11.2 21.3 8.4 Disease Staging LOS Base .... 100.0 158.5 40.7 65.4 215.7 149.3 Resource use Disease Staging Resource Demand Base = 100.0 127.4 114.6 168.8 263.7 129.7 PMCs Resource Intensity Scale (RIS) Base = 1.00 1.712 3.226 1.957 3.232 1.66 charge diagnosis and procedure codes. We scored the remaining code-based measure , the Comorbidity Index, ourselves, us ing the Deyo modification 1° of the Charlson index, l l which employs only diagnosis codes.

Duration of blood pressure elevation in accurately predicting surgical cure of renovascular hypertension

Among 110 patients who underwent corrective surgery for unilateral renovascular hypertension, we found that a preoperative of hypertension was a highly important predictor of postoperative achievement of normotension. Those with less than a 5-year history of hypertension experienced 78% incidence of successful outcome, compared to such a salutary frequency of only 25% in patients with longer hypertension duration. Although the best renal vein renin (RVR) boundary ratio (1.4) was less predictive of overall surgical success in the population studied, the prognostic value of this test improved considerably when analysis of RVR ratio results were confined to patients not receiving renin-suppressing agents during RVR sampling and who had technically satisfactory operations. Highest surgical benefit rate occurred in the group of patient with both short duration of hypertension and high RVR ratio. Conversely, patients with long hypertension duration and low RVR ratio exhibited lowest surgical success frequency. Therefore, duration of hypertension is hereby shown to be an important factor in the preoperative evaluation of appropriate management of patients with renovascular hypertension.

Hospital readmission rates: the impacts of age, payer, and mental health diagnoses.

We examine impacts of age, payer, and mental health conditions upon hospital readmissions and the comparability of same-hospital and multiple-hospital readmission rates. Medicaid primary payment and extreme age are associated with significantly higher readmission rates. We find low correlation between same-hospital and multiple-hospital readmission rates and identify urban hospitals with high proportions of Medicaid patients and mental health admissions as factors driving the use of multiple hospitals within readmission chains. Hospital payment incentives and performance measures using readmission rates will be distorted if factors leading to higher readmission rates are ignored, or if readmissions to different hospitals cannot be identified.

Paying for Outcomes, Not Performance: Lessons from the Medicare Inpatient Prospective Payment System

Drawing on lessons learned from the implementation of the Medicare Inpatient Prospective Payment System (IPPS), the authors propose principles for the design and implementation of a hospital payment system based on paying for outcomes.

Developing a prospective payment system based on episodes of care.

A patient-centered approach to defining episodes of care around a hospitalization can provide the basis for creating expanded bundles of services that can be used as the basis of payment. Paying by episodes of care strengthens the incentive to providers to deliver care efficiently. A hospital-based episode of care prospective payment system can be phased in over time by gradually expanding the services and the time period included in the episode. Establishing equitable prospective episode payment amounts requires that the severity of illness of the patient during the hospitalization and the chronic disease burden of the patient be taken into account.

Hospital Complications: Linking Payment Reduction to Preventability

The Center for Medicare & Medicaid Services (CMS) policy of denying payment for certain in-hospital complications should be modified, given that complications are not always preventable.

Rethinking Medicare Payment Adjustments for Quality

Payment reforms aimed at linking payment and quality have largely been based on the adherence to process measures. As a result, the attempt to pay for value is getting lost in an overly complex attempt to measure value. The “Incentivizing Health Care Quality Outcomes Act of 2014” (HR 5823) proposes to replace the existing patchwork of process and outcomes quality measures with a uniform, coordinated, and comprehensive outcomes-based quality measurement system. The Outcomes Act represents a shift in payment policy toward getting value instead of an increasingly complex attempt to measure value.