Predicting Unplanned Health Care Utilization and Cost: Comparing Patient-reported Outcomes Measurement Information System and Claims.

OBJECTIVES: There is little literature describing if and how payers are utilizing patient-reported outcomes to predict future costs. This study assessed if Patient-reported Outcomes Measurement Information System (PROMIS) domain scores, collected in routine practice at neurology clinics, improved payer predictive models for unplanned care utilization and cost. STUDY DESIGN: Retrospective cohort analysis of private Health Plan-insured patients with visits at 18 Health Plan-affiliated neurology clinics. METHODS: PROMIS domains (Anxiety v1.0, Cognitive Function Abilities v2.0, Depression v1.0, Fatigue v1.0, Pain Interference v1.0, Physical Function v2.0, Sleep Disturbance v1.0, and Ability to Participate in Social Roles and Activities v2.0) are collected as part of routine care. Data from patients' first PROMIS measures between June 27, 2018 and April 16, 2019 were extracted and combined with claims data. Using (1) claims data alone and (2) PROMIS and claims data, we examined the association of covariates to utilization (using a logit model) and cost (using a generalized linear model). We evaluated model fit using area under the receiver operating characteristic curve (for unplanned care utilization), akaike information criterion (for unplanned care costs), and sensitivity and specificity in predicting top 15% of unplanned care costs. RESULTS: Area under the receiver operating curve values were slightly higher, and akaike information criterion values were similar, for PROMIS plus claims covariates compared with claims alone. The PROMIS plus claims model had slightly higher sensitivity and equivalent specificity compared with claims-only models. CONCLUSION: One-time PROMIS measure data combined with claims data slightly improved predictive model performance compared with claims alone, but likely not to an extent that indicates improved practical utility for payers.

Using electronic clinical quality measure reporting for public health surveillance.

By June 2013, three fourths of office-based practicing physicians in the United States had adopted some form of electronic health record (EHR) system. With greater EHR use, more health data are linked with available patient demographic information in a format that is easily retrievable and collected at the point of care. This highlights the potential of electronic clinical quality measure (CQM) reporting data for use in monitoring population health for those receiving health care services. To assess this possibility, electronic CQM data that were submitted to the Medicare EHR Incentive Program were analyzed to assess provider progress toward achieving blood pressure control among their patients with hypertension. Approximately 63,000 health care providers reported at least 1 time over 3 years, representing approximately 17 million patients with hypertension. On average, 62% of patients with hypertension had controlled blood pressure. Use of EHR data for public health surveillance could streamline reporting, facilitating more timely and possibly more complete data collection in key areas of public health concern.

Clustering patterns of comorbidities associated with in-hospital death in hospitalizations of US adults with venous thromboembolism.

BACKGROUND: Venous thromboembolism (VTE) is a significant source of mortality, morbidity, disability, and impaired health-related quality of life in the world. OBJECTIVE: We aimed to evaluate the clustering patterns and associations of 29 comorbidities with in-hospital death among adult hospitalizations with a diagnosis of VTE in the United States by analyzing data from the 2009 Nationwide Inpatient Sample. METHODS: This cross-sectional study included 153,124 adult hospitalizations with a diagnosis of VTE. Adjusted rate ratios and 95% confidence intervals (CI) for in-hospital death were generated by using multivariable log-linear regression models to measure independent associations between comorbidities and in-hospital death. RESULTS: We estimated that 44,200 in-hospital deaths occurred in 2009 among 773,273 US adult hospitalizations with a diagnosis of VTE. Subgroups of hospitalizations with comorbidities of "congestive heart failure," "chronic pulmonary disease," "coagulopathy," "liver disease," "lymphoma," "fluid and electrolyte disorders," "metastatic cancer," "peripheral vascular disorders," "pulmonary circulation disorders," "renal failure," "solid tumor without metastasis," or "weight loss" were positively and independently associated with 1.07 (95% CI: 1.02-1.12 ) to 2.06 (95% CI: 1.97-2.16) times increased likelihoods of in-hospital death, when compared to those without the corresponding comorbidities. The clustering patterns of these comorbidities by 4 disease categories (i.e., "cancer," "cardiovascular/respiratory/blood," "gastrointestinal/urologic," and "nutritional/bodyweight") were associated with 2.74 to 10.28 times increased likelihoods of in-hospital death, as compared to hospitalizations without any of these comorbidities. The overall increase in the cumulative number of comorbidities corresponded to significantly elevated risks (P-trend<0.01) for in-hospital death among hospitalizations with a diagnosis of VTE. CONCLUSION: The presence of multiple comorbidities is ubiquitous among hospitalizations of adults with VTE and among in-hospital deaths with VTE in the United States. The findings of our study further suggest that, among hospitalizations of adults with VTE, the presence of certain comorbidities or clustering of these comorbidities significantly elevates the risk of in-hospital death.

Measuring Patient Safety: The Medicare Patient Safety Monitoring System (Past, Present, and Future).

ABSTRACT: The explicit declaration in the landmark 1999 Institute of Medicine report "To Err Is Human" that, in the United States, 44,000 to 98,000 patients die each year as a consequence of "medical errors" gave widespread validation to the magnitude of the patient safety problem and catalyzed a number of U.S. federal government programs to measure and improve the safety of the national healthcare system. After more than 10 years, one of those federal programs, the Medicare Patient Safety Monitoring System (MPSMS), has reached a level of maturity and stability that has made it useful for the consistent measurement of the safety of inpatient care. The MPSMS is a chart review-based national patient safety surveillance system that provides rates of 21 specific hospital inpatient adverse event measures, which have been divided into 4 clinical domains (general, hospital-acquired infections, postprocedure adverse events, and adverse drug events) for analysis. The 2014 MPSMS national sample was drawn from 1109 hospitals and includes approximately 20,000 medical records of patients admitted to the hospital (all payors) for at least 1 of the 4 conditions of congestive heart failure, acute myocardial infarction, pneumonia, and major surgical procedures as defined by the Centers for Medicare and Medicaid Services Surgical Care Improvement Project. The MPSMS is now going through a major transformation to capture additional types of adverse events and is being redeveloped as the Quality and Safety Review System (QSRS). As an example of this transformation, QSRS will electronically import electronic data, which are standardized according to the Centers for Medicare and Medicaid Services billing definitions and will be updated and evolve over time to incorporate expanded standardized data available from electronic health records. This article reviews the development of MPSMS, the strengths and limitations of MPSMS, and expected future directions in patient safety measurement, focusing on those issues that are informing the development and implementation of QSRS.

Physician satisfaction with health plans: results from a national survey.

OBJECTIVES: Physician satisfaction is associated with patient satisfaction, adherence to treatment recommendations, and quality. However, burnout is prevalent, and physician experience with health plans is likely a key contributor. We explored physician satisfaction with health plans and assessed physician and plan characteristics associated with greater satisfaction. STUDY DESIGN: Cross-sectional analysis of physician satisfaction surveys for US health plans in 2016. METHODS: We assessed the association between health plan/physician characteristics and physician satisfaction domains using multivariable linear regression. The following satisfaction domains were outcomes of interest, measured by 5-point Likert scales: overall health plan rating, finance, utilization/quality management, network/care coordination, pharmacy, call center, provider relations, and recommendation of the plan to others' practices. RESULTS: We analyzed surveys from 3158 physicians on 74 health plans, representing a 12.6% response rate. We observed highest satisfaction in overall plan rating, finance, and call center domains (adjusted means = 3.25) and lowest satisfaction in the pharmacy domain (adjusted mean = 3.02). The largest and smallest plans and vertically integrated plans had the highest satisfaction; 76% and 66% of physicians recommended vertically integrated plans and non-vertically integrated plans, respectively, to others (P <.001). Solo practitioners rated overall plan rating, finance, utilization/quality management, and pharmacy domains more favorably than did physicians in larger practices, whereas primary care physicians rated overall plan rating, finance, and utilization/quality management more favorably than did specialists. CONCLUSIONS: Our findings demonstrate opportunity to improve physician satisfaction with health plans, specifically in pharmacy/formulary management. As provider satisfaction is increasingly recognized as a critical outcome, our findings highlight intervention targets.

Recommendations for selecting drug-drug interactions for clinical decision support.

PURPOSE: Recommendations for including drug-drug interactions (DDIs) in clinical decision support (CDS) are presented. SUMMARY: A conference series was conducted to improve CDS for DDIs. A work group consisting of 20 experts in pharmacology, drug information, and CDS from academia, government agencies, health information vendors, and healthcare organizations was convened to address (1) the process to use for developing and maintaining a standard set of DDIs, (2) the information that should be included in a knowledge base of standard DDIs, (3) whether a list of contraindicated drug pairs can or should be established, and (4) how to more intelligently filter DDI alerts. We recommend a transparent, systematic, and evidence-driven process with graded recommendations by a consensus panel of experts and oversight by a national organization. We outline key DDI information needed to help guide clinician decision-making. We recommend judicious classification of DDIs as contraindicated and more research to identify methods to safely reduce repetitive and less-relevant alerts. CONCLUSION: An expert panel with a centralized organizer or convener should be established to develop and maintain a standard set of DDIs for CDS in the United States. The process should be evidence driven, transparent, and systematic, with feedback from multiple stakeholders for continuous improvement. The scope of the expert panel's work should be carefully managed to ensure that the process is sustainable. Support for research to improve DDI alerting in the future is also needed. Adoption of these steps may lead to consistent and clinically relevant content for interruptive DDIs, thus reducing alert fatigue and improving patient safety.

Assessing the potential adoption and usefulness of concurrent, action-oriented, electronic adverse drug event triggers designed for the outpatient setting.

BACKGROUND: Adverse drug event (ADE) detection is an important priority for patient safety research. Trigger tools have been developed to help identify ADEs. In previous work we developed seven concurrent, action-oriented, electronic trigger algorithms designed to prompt clinicians to address ADEs in outpatient care. OBJECTIVES: We assessed the potential adoption and usefulness of the seven triggers by testing the positive predictive validity and obtaining stakeholder input. METHODS: We adapted ADE triggers, "bone marrow toxin-white blood cell count (BMT-WBC)," "bone marrow toxin - platelet (BMT-platelet)," "potassium raisers," "potassium reducers," "creatinine," "warfarin," and "sedative hypnotics," with logic to suppress flagging events with evidence of clinical intervention and applied the triggers to 50,145 patients from three large health care systems. Four pharmacists assessed trigger positive predictive value (PPV) with respect to ADE detection (conservatively excluding ADEs occurring during clinically appropriate care) and clinical usefulness (i.e., whether the trigger alert could change care to prevent harm). We measured agreement between raters using the free kappa and assessed positive PPV for the trigger's detection of harm, clinical usefulness, and both. Stakeholders from the participating health care systems rated the likelihood of trigger adoption and the perceived ease of implementation. FINDINGS: Agreement between pharmacist raters was moderately high for each ADE trigger (kappa free > 0.60). Trigger PPVs for harm ranged from 0 (Creatinine, BMT-WBC) to 17 percent (potassium raisers), while PPV for care change ranged from 0 (WBC) to 60 percent (Creatinine). Fifteen stakeholders rated the triggers. Our assessment identified five of the seven triggers as good candidates for implementation: Creatinine, BMT-Platelet, Potassium Raisers, Potassium Reducers, and Warfarin. CONCLUSIONS: At least five outpatient ADE triggers performed well and merit further evaluation in outpatient clinical care. When used in real time, these triggers may promote care changes to ameliorate patient harm.

Applying trigger tools to detect adverse events associated with outpatient surgery.

OBJECTIVE: The objective of this study is to evaluate the performance of 5 triggers to detect adverse events (AEs) associated with outpatient surgery. Triggers use surveillance algorithms derived from clinical logic to flag cases where AEs have most likely occurred. Current efforts to detect AEs have focused primarily on the inpatient setting, despite the increase in outpatient surgery in all health care settings. METHODS: Using trigger logic, we retrospectively evaluated data from 3 large health care systems' electronic medical records. Patients were eligible for inclusion if they had an outpatient (same-day) surgery in 2007 and at least 1 clinical note in the 6 months after the surgery. Two nurse abstractors reviewed a sample of trigger-flagged cases from each health care system. After reaching interrater reliability targets (κ > 0.60), we calculated the positive predictive value (PPV) of each trigger and the confidence interval of the estimate. RESULTS: The surgical triggers flagged between 1% and 22% of the outpatient surgery cases, with a wide range in PPVs (6.0%-62.0%). The pulmonary embolism and deep vein thrombosis and emergency department triggers had the lowest proportion of flagged cases along with the highest PPVs, showing the most promise for screening cases with a high probability of AE occurrence. CONCLUSIONS: Triggers may be useful in identifying a narrow set of surgeries for further review to determine if a surgical AE occurred, complementing existing tools and initiatives used to detect AEs. Improved detection of AEs in outpatient surgery should help target potential areas for quality improvement.

Development of trigger tools for surveillance of adverse events in ambulatory surgery.

BACKGROUND: The trigger tool methodology uses clinical algorithms applied electronically to 'flag' medical records where adverse events (AEs) have most likely occurred. The authors sought to create surgical triggers to detect AEs in the ambulatory care setting. METHODS: Four consecutive steps were used to develop ambulatory surgery triggers. First, the authors conducted a comprehensive literature review for surgical triggers. Second, a series of multidisciplinary focus groups (physicians, nurses, pharmacists and information technology specialists) provided user input on trigger selection. Third, a clinical advisory panel designed an initial set of 10 triggers. Finally, a three-phase Delphi process (surgical and trigger tool experts) evaluated and rated the suggested triggers. RESULTS: The authors designed an initial set of 10 surgical triggers including five global triggers (flagging medical records for the suspicion of any AE) and five AE-specific triggers (flagging medical records for the suspicion of specific AEs). Based on the Delphi rating of the trigger's utility for system-level interventions, the final triggers were: (1) emergency room visit(s) within 21 days from surgery; (2) unscheduled readmission within 30 days from surgery; (3) unscheduled procedure (interventional radiological, urological, dental, cardiac or gastroenterological) or reoperation within 30 days from surgery; (4) unplanned initial hospital length of stay more than 24 h; and (5) lower-extremity Doppler ultrasound order entry and ICD code for deep vein thrombosis or pulmonary embolus within 30 days from surgery. CONCLUSION: The authors therefore propose a systematic methodology to develop trigger tools that takes into consideration previously published work, end-user preferences and expert opinion.