Dataset Information

Does adding clinical data to administrative data improve agreement among hospital quality measures?

ABSTRACT: Hospital performance measures based on patient mortality and readmission have indicated modest rates of agreement. We examined if combining clinical data on laboratory tests and vital signs with administrative data leads to improved agreement with each other, and with other measures of hospital performance in the nation's largest integrated health care system.We used patient-level administrative and clinical data, and hospital-level data on quality indicators, for 2007-2010 from the Veterans Health Administration (VA). For patients admitted for acute myocardial infarction (AMI), heart failure (HF) and pneumonia we examined changes in hospital performance on 30-d mortality and 30-d readmission rates as a result of adding clinical data to administrative data. We evaluated whether this enhancement yielded improved measures of hospital quality, based on concordance with other hospital quality indicators.For 30-d mortality, data enhancement improved model performance, and significantly changed hospital performance profiles; for 30-d readmission, the impact was modest. Concordance between enhanced measures of both outcomes, and with other hospital quality measures - including Joint Commission process measures, VA Surgical Quality Improvement Program (VASQIP) mortality and morbidity, and case volume - remained poor.Adding laboratory tests and vital signs to measure hospital performance on mortality and readmission did not improve the poor rates of agreement across hospital quality indicators in the VA.Efforts to improve risk adjustment models should continue; however, evidence of validation should precede their use as reliable measures of quality.

SUBMITTER: Hanchate AD

PROVIDER: S-EPMC5772776 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Does adding clinical data to administrative data improve agreement among hospital quality measures?

Hanchate Amresh D AD Stolzmann Kelly L KL Rosen Amy K AK Fink Aaron S AS Shwartz Michael M Ash Arlene S AS Abdulkerim Hassen H Pugh Mary Jo V MJV Shokeen Priti P Borzecki Ann A

Healthcare (Amsterdam, Netherlands) 20161205 3

<h4>Background</h4>Hospital performance measures based on patient mortality and readmission have indicated modest rates of agreement. We examined if combining clinical data on laboratory tests and vital signs with administrative data leads to improved agreement with each other, and with other measures of hospital performance in the nation's largest integrated health care system.<h4>Methods</h4>We used patient-level administrative and clinical data, and hospital-level data on quality indicators, ...[more]

PMID: 27932261

Similar Datasets

Project description:BackgroundRising national cesarean section rates (CSRs) and unexplained inter-hospital differences in CSRs, led national and international bodies to select CSR as a quality indicator. Using hospital discharge abstracts, we aimed to document in Belgium (1) inter-hospital differences in CSRs among low risk deliveries, (2) a national upward CSR trend, (3) lack of better neonatal outcomes in hospitals with high CSRs, and (4) possible under-use of CS.MethodsWe defined a population of low risk deliveries (singleton, vertex, full-term, live born, <4500 g, >2499 g). Using multivariable logistic regression techniques, we provided degrees of evidence regarding the observed departure ([relative risk-1]*100) of each hospital (N = 107) from the national CSR and its trend. To determine a benchmark, we defined three CSR groups (high, average and low) and compared them regarding 1 minute Apgar scores and other neonatal endpoints. An anonymous feedback is provided to the hospitals, the College of Physicians (with voluntary disclosure of the outlying hospitals for quality improvement purposes) and to the policy makers.ResultsCompared with available information, the completeness and accuracy of the data, regarding the variables selected to determine our study population, showed adequate. Important inter-hospital differences were found. Departures ranged from -65% up to +75%, and 9 "high CSR" and 13 "low CSR" outlying hospitals were identified. We observed a national increasing trend of 1.019 (95%CI [1.015; 1.022]) per semester, adjusted for age groups. In the "high CSR" group 1 minute Apgar scores <4 were over-represented in the subgroup of vaginal deliveries, suggesting CSs not carried out for medical reasons. Under-use of CS was also observed. Given their questionable completeness, except Apgar scores, our neonatal results, showing a significant association of CS with adverse neonatal endpoints, are to be cautiously interpreted. Taking the available evidence into account, the "Average CSR" group seemed to be the best benchmark candidate.ConclusionRather than firm statements about quality of care, our results are to be considered a useful screening. The inter-hospital differences in CSR, the national CS upward trend, the indications of over-use and under-use, the geographically different obstetric patterns and the admission day-related concentration of deliveries, whether or not by CS, may trigger initiatives aiming at improving quality of care.

Project description:ContextMany of the publicly available health quality report cards are based on administrative data. ICD-9-CM codes in administrative data are not date stamped to distinguish between medical conditions present at the time of hospital admission and complications, which occur after hospital admission. Treating complications as preexisting conditions gives poor-performing hospitals "credit" for their complications and may cause some hospitals that are delivering low-quality care to be misclassified as average- or high-performing hospitals.ObjectiveTo determine whether hospital quality assessment based on administrative data is impacted by the inclusion of condition present at admission (CPAA) modifiers in administrative data as a date stamp indicator.Design, setting, and patientsRetrospective cohort study based on 648,866 inpatient admissions between 1998 and 2000 for coronary artery bypass graft (CABG) surgery, coronary angioplasty (PTCA), carotid endarterectomy (CEA), abdominal aortic aneurysm (AAA) repair, total hip replacement (THR), acute MI (AMI), and stroke using the California State Inpatient Database which includes CPAA modifiers. Hierarchical logistic regression was used to create separate condition-specific risk adjustment models. For each study population, one model was constructed using only secondary diagnoses present at admission based on the CPAA modifier: "date stamp" model. The second model was constructed using all secondary diagnoses, ignoring the information present in the CPAA modifier: the "no date stamp model." Hospital quality was assessed separately using the "date stamp" and the "no date stamp" risk-adjustment models.ResultsForty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 40 percent of the THR hospitals, and 33 percent of the AMI hospitals identified as low-performance hospitals by the "date stamp" models were not classified as low-performance hospitals by the "no date stamp" models. Fifty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 50 percent of the CEA hospitals, and 36 percent of the AMI hospitals identified as low-performance hospitals by the "no date stamp" models were not identified as low-performance hospitals by the "date stamp" models. The inclusion of the CPAA modifier had a minor impact on hospital quality assessment for AAA repair, stroke, and CEA.ConclusionThis study supports the hypothesis that the use of routine administrative data without date stamp information to construct hospital quality report cards may result in the mis-identification of hospital quality outliers. However, the CPAA modifier will need to be further validated before date stamped administrative data can be used as the basis for health quality report cards.

Project description:BackgroundThe goal of regionalized perinatal care, specifically levels of maternal care, is to improve maternal outcomes through risk-appropriate obstetric care. Studies of levels of maternal care are limited by current approaches to identify a hospital's level of care, often relying on hospital self-reported data, which is expensive and challenging to collect and validate. The study objective was to develop an empiric approach to determine a hospital's level of maternal care using administrative data reflective of the patient care provided and apply this approach to describe the levels of maternal care available over time.MethodsRetrospective cohort study of mother-infant dyads who delivered in California, Missouri, and Pennsylvania hospitals from 2000 to 2009. Linked mother-infant administrative records with an infant born at 24-44 weeks' gestation and a birth weight of 400-8000 g were included. Using the American College of Obstetricians and Gynecologists and the Society for Maternal Fetal Medicine descriptions of levels of maternal care, four levels were classified based on the appropriate location of care for patients with specific medical or pregnancy conditions. Individual hospitals were assigned a level of maternal care annually based on the volume of patients who delivered reflective of the four classified levels as determined by International Classification of Diseases and Current Procedural Terminology.ResultsBased on the included 6,895,000 mother-infant dyads, the obstetric hospital levels of maternal care I, II, III and IV were identified. High-risk patients more frequently delivered in hospitals with higher level maternal care, accounting for 8.9, 10.9, 13.8, and 16.9% of deliveries in level I, II, III and IV hospitals, respectively. The total number of obstetric hospitals decreased over the study period, while the proportion of hospitals with high-level (level III or IV) maternal care increased. High-level hospitals were located in more densely populated areas.ConclusionIdentification of the level of maternal care, independent of hospital self-reported variables, is feasible using administrative data. This empiric approach, which accounts for changes in hospitals over time, is a valuable framework for perinatal researchers and other stakeholders to inexpensively identify measurable benefits of levels of maternal care and characterize where specific patient populations receive care.

Project description:BACKGROUND:High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. METHODS:The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. RESULTS:The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median?=?26.0%). CONCLUSIONS:Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.

Dataset Information

Does adding clinical data to administrative data improve agreement among hospital quality measures?

Publications

Does adding clinical data to administrative data improve agreement among hospital quality measures?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure