The Sentinel of Managed Care is Predicting Readmissions
Submitted by Dr. JG Kaplan on Fri, 2010-02-12 17:37
Identifying Potentially Preventable Readmissions Obeys Willie Sutton's Law--Go where the money is!
On 12/04/09 4:44 AM, jgk wrote:
Jon Eisenhandler, PhD 3M Health Information Systems 100 Barnes Rd. Wallingford, CT 06492 phone: 203-949-6662; fax: 203-949-6331
Jon,
We agree that savings can come from efficient, effective care and equitable, universal coverage. What is your take on operationalizing comparative effectiveness research (CER)? I want a comprehensive methodology that covers not ony CER, but also cost-effectiveness analysis, quality of care assessment, and as a general, longitudinal and comparable* view, episodes of care (EOC). I know it's a tall order and others do some of this (e.g., Symmetry, ETG's) but, I want to know 3M's take on this.
* acuity or case-mix adjustment
From Jon
Jeff, please look at these three files for successful efforts we have had along those lines:
"Given the increasing pressure to control health care costs and improve quality, and increasing public and governmental scrutiny of both, financial incentives associated with quality measures in general, and hospital readmission rates in particular, will only increase. The effectiveness of these efforts will depend on the integrity of the data and the validity of the methods used in any performance-based payment systems. This study suggests that adequate risk stratification based on patient type and severity of illness as well as identification of those readmissions that are potentially preventable are critical to the fairness and usefulness of any evaluations and comparisons of hospital readmission rates."
2. Hospital 30-Day Heart Failure Readmission Measure Methodology
"We present a hierarchical logistic regression model for 30-day readmission after HF hospitalization that is based on administrative data and is suitable for public reporting. The model is a strong surrogate for a similar “gold standard” model based on chart data. The approach employs a grouper of 15,000 ICD-9 codes that is in the public domain yielding clinically coherent variables."3. Rehospitalizations among Patients in the Medicare Fee-for-Service Program
"Our analysis generally confirms Anderson and Steinberg’s findings regarding the value of demographic factors in predicting the risk of rehospitalization, 6 but it shows that previous rehospitalization, a longer index hospitalization as compared with the norm for the DRG, the need for dialysis, and the DRG to which the patient is assigned at the end of the stay are more powerful predictors. However, when the typical patient has almost two chances in three of being rehospitalized or of dying within a year after discharge, it is probably wiser to consider all Medicare patients as having a high risk of rehospitalization. For example, ensuring that a follow-up appointment with a physician is scheduled for every patient before he or she leaves the hospital is probably more efficient than trying to identify high-risk patients and arranging follow-up care just for them."
Other References:
"Reimbursement Methodologies for Healthcare Services," pub. by American Health Information Management Association's (AHIMA) and 3M, All Products
"Cost-cutting Proposals," published here.
Missed that low hanging
Missed that low hanging fruit--Our pathetic inability to prevent readmissions
ABSTRACT
Context Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison.
Objective To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use.
Data Sources and Study Selection The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in both derivation and validation cohorts.
Data Extraction Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection.
Data Synthesis Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health.
Conclusions Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.
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