Naessens, James M.

BACKGROUNDThe use of rapid response systems (RRS), which were designed to bring clinicians with critical care expertise to the bedside to prevent unnecessary deaths, has increased. RRS rely on accurate detection of acute deterioration events. Early warning scores (EWS) have been used for this purpose but were developed using heterogeneous populations. Predictive performance may differ in medical vs surgical patients.OBJECTIVETo evaluate the performance of published EWS in medical vs surgical patient populations.DESIGNRetrospective cohort study.SETTINGTwo tertiary care academic medical center hospitals in the Midwest totaling more than 1500 beds.PATIENTSAll patients discharged from January to December 2011.INTERVENTIONNone.MEASUREMENTSTime‐stamped longitudinal database of patient variables and outcomes, categorized as surgical or medical. Outcomes included unscheduled transfers to the intensive care unit, activation of the RRS, and calls for cardiorespiratory resuscitation (“resuscitation call”). The EWS were calculated and updated with every new patient variable entry over time. Scores were considered accurate if they predicted an outcome in the following 24 hours.RESULTSAll EWS demonstrated higher performance within the medical population as compared to surgical: higher positive predictive value (P < .0001 for all scores) and sensitivity (P < .0001 for all scores). All EWS had positive predictive values below 25%.CONCLUSIONSThe overall poor performance of the evaluated EWS was marginally better in medical patients when compared to surgical patients.

AbstractRationale, aims and objectivesDiagnostic uncertainty is often encountered in a medical practice. Patients with ambiguous, uncertain, and undiagnosed problems are frequently referred for second opinions. Comparing referral diagnoses to final diagnoses provides an opportunity to determine how frequently final diagnoses vary and changes the direction of medical care.MethodsA retrospective study was done at a single academic medical center using a sample of 286 patients referred by physician assistants, nurse practitioners, and physicians from primary care practices from January 1, 2009 to December 31, 2010. Patients' referral and final diagnoses were compared and classified into 1 of 3 categories: referral diagnosis and final diagnosis the same, referral diagnosis better defined/refined, and referral diagnosis distinctly different from final diagnosis. Episode costs for the respective categories were calculated for the referral visit and services that occurred at our facility within the first 30 days.ResultsIn 12% (36/286) of cases, referral diagnoses were the same as final diagnoses. Final diagnoses were better defined/refined in 66% (188/286) of cases; but in 21% of cases (62/286), final diagnoses were distinctly different than referral diagnoses. Total costs for cases in category 3 (different final diagnoses) were significantly higher than costs for cases in category 1 (P = .0001) and category 2 (P = <.0001).ConclusionReferrals to advanced specialty care for undifferentiated problems are an essential component of patient care. Without adequate resources to handle undifferentiated diagnoses, a potential unintended consequence is misdiagnoses resulting in treatment delays and complications leading to more costly treatments.

The majority of quality measures used to assess providers and hospitals are based on easily obtained data, focused on a few dimensions of quality, and developed mainly for primary/community care and population health. While this approach supports efforts focused on addressing the triple aim of health care, many current quality report cards and assessments do not reflect the breadth or complexity of many referral center practices. In this article, the authors highlight the differences between population health efforts and referral care and address issues related to value measurement and performance assessment. They discuss why measures may need to differ across the three levels of care (primary/community care, secondary care, complex care) and illustrate the need for further risk adjustment to eliminate referral bias. With continued movement toward value-based purchasing, performance measures and reimbursement schemes need to reflect the increased level of intensity required to provide complex care. The authors propose a framework to operationalize value measurement and payment for specialty care, and they make specific recommendations to improve performance measurement for complex patients. Implementing such a framework to differentiate performance measures by level of care involves coordinated efforts to change both policy and operational platforms. An essential component of this framework is a new model that defines the characteristics of patients who require complex care and standardizes metrics that incorporate those definitions.

The Surviving Sepsis Campaign guidelines are designed to decrease mortality through consistent application of a 7-element bundle. This study evaluated the impact of improvement in bundle adherence using a time-series analysis of compliance with the bundle elements before and after interventions intended to improve the process, while also looking at hospital mortality. This article describes interventions used to improve bundle compliance and hospital mortality in patients admitted through the emergency department with sepsis, severe sepsis, or septic shock. Quality improvement methodology was used to develop high-impact interventions that led to dramatically improved adherence to the Surviving Sepsis Campaign guidelines bundle. Improved performance was associated with a significant decrease in the in-hospital mortality of severe sepsis patients presenting to the emergency department.

Objective: No risk prediction model is currently available to measure patient’s probability for readmission to the pediatric intensive care unit (PICU). This retrospective case–control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]). Design: Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation. Results: Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50. Conclusion: We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.

To reliably assess quality, a standardized electronic approach is needed to identify bleeding events. The study aims were the following: (1) clinically validate an electronic health record–based algorithm for bleeding and (2) assess interrater results to determine validity and reliability. Data were analyzed before and after implementation of a pharmacist-managed warfarin protocol. Bleeding was based on ≥2 of 3 criteria: (1) diagnosis indicating bleeding, (2) lab value decrease suggesting bleeding, and (3) blood product use. All suspected bleeds (234) and a sample (58) not meeting criteria were compared with clinical review. There were 234 bleeding cases identified electronically. Reviewer agreement was 78.2% (κ = 0.565). Algorithm sensitivity was 93.9% and positive predictive value 46.2%. Algorithm identification was least accurate for those with only 2 criteria but good for those with all criteria. This study supports using multiple electronic criteria to identify bleeding events. However, cases having exactly 2 criteria may require manual review for validation.

Purpose To examine medical student attitudes toward cost-conscious care and whether regional health care intensity is associated with reported exposure to physician role-modeling behaviors related to cost-conscious care. Method Students at 10 U.S. medical schools were surveyed in 2015. Thirty-five items assessed attitudes toward, perceived barriers to and consequences of, and observed physician role-modeling behaviors related to cost-conscious care (using scales for cost-conscious and potentially wasteful behaviors; Cronbach alphas of 0.82 and 0.81, respectively). Regional health care intensity was measured using Dartmouth Atlas End-of-Life Chronic Illness Care data: ratio of physician visits per decedent compared with the U.S. average, ratio of specialty to primary care physician visits per decedent, and hospital care intensity index. Results Of 5,992 students invited, 3,395 (57%) responded. Ninety percent (2,640/2,932) agreed physicians have a responsibility to contain costs. However, 48% (1,1416/2,960) thought ordering a test is easier than explaining why it is unnecessary, and 58% (1,685/2,928) agreed ordering fewer tests will increase the risk of malpractice litigation. In adjusted linear regression analyses, students in higher-health-care-intensity regions reported observing significantly fewer cost-conscious role-modeling behaviors: For each one-unit increase in the three health care intensity measures, scores on the 21-point cost-conscious role-modeling scale decreased by 4.4 (SE 0.7), 3.2 (0.6), and 3.9 (0.6) points, respectively (all P < .001). Conclusions Medical students endorse barriers to cost-conscious care and encounter conflicting role-modeling behaviors, which are related to regional health care intensity. Enhancing role modeling in the learning environment may help prepare future physicians to address health care costs.

Numerical laboratory data at admission have been proposed for enhancement of inpatient predictive modeling from administrative claims. In this study, predictive models for inpatient/30-day postdischarge mortality and for risk-adjusted prolonged length of stay, as a surrogate for severe inpatient complications of care, were designed with administrative data only and with administrative data plus numerical laboratory variables. A comparison of resulting inpatient models for acute myocardial infarction, congestive heart failure, coronary artery bypass grafting, and percutaneous cardiac interventions demonstrated improved discrimination and calibration with administrative data plus laboratory values compared to administrative data only for both mortality and prolonged length of stay. Improved goodness of fit was most apparent in acute myocardial infarction and percutaneous cardiac intervention. The emergence of electronic medical records should make the addition of laboratory variables to administrative data an efficient and practical method to clinically enhance predictive modeling of inpatient outcomes of care.

Predictive modeling for postdischarge outcomes of inpatient care has been suboptimal. This study evaluated whether admission numerical laboratory data added to administrative models from New York and Minnesota hospitals would enhance the prediction accuracy for 90-day postdischarge deaths without readmission (PD-90) and 90-day readmissions (RA-90) following inpatient care for cardiac patients. Risk-adjustment models for the prediction of PD-90 and RA-90 were designed for acute myocardial infarction, percutaneous cardiac intervention, coronary artery bypass grafting, and congestive heart failure. Models were derived from hospital claims data and were then enhanced with admission laboratory predictive results. Case-level discrimination, goodness of fit, and calibration were used to compare administrative models (ADM) and laboratory predictive models (LAB). LAB models for the prediction of PD-90 were modestly enhanced over ADM, but negligible benefit was seen for RA-90. A consistent predictor of PD-90 and RA-90 was prolonged length of stay outliers from the index hospitalization.

ObjectivesPerformance measurement is used by health care providers, payers, and patients. Historically accomplished using administrative data, registries are used increasingly to track and improve care. We assess how measured diabetes care quality differs when calculated using claims versus registry.Data Sources/Study SettingCross‐sectional analysis of administrative claims and electronic health records (EHRs) of patients in a multispecialty integrated health system in 2012 (n = 368,883).Study DesignWe calculated percent of patients attaining glycohemoglobin <8.0 percent, LDL cholesterol <100 mg/dL, blood pressure <140/90 mmHg, and nonsmoking (D4) in cohorts, identified by Medicare Accountable Care Organization/Minnesota Community Measures (ACO‐MNCM; claims‐based), Healthcare Effectiveness Data and Information Set (HEDIS; claims‐based), and registry (EHR‐based).Data Collection/Extraction MethodsClaims were linked to EHR to create a dataset of performance‐eligible patients.Principal FindingsACO‐MNCM, HEDIS, and registry identified 6,475, 6,989, and 6,425 measurement‐eligible patients. Half were common among the methods; discrepancies were due to attribution, age restriction, and encounter requirements. D4 attainment was lower in ACO‐MNCM (36.09 percent) and HEDIS (37.51 percent) compared to registry (43.74 percent) cohorts.ConclusionsRegistry‐ and claims‐based performance measurement methods identify different patients, resulting in different rates of quality metric attainment with implications for innovative population health management.