Reducing burnout and enhancing work engagement among clinicians: The Minnesota experience.

BACKGROUND: The Minnesota Hospital Association (MHA) recognized the impact that burnout and disengagement had on the clinician population. A clinician task force developed a conceptual framework, followed by annual surveys and a series of interventions. Features of the job demands-resources model were used as the conceptual underpinning to this analysis. PURPOSE: The aim of this study was to assess the applicability of a clinician-driven conceptual model in understanding burnout and work engagement in the state of Minnesota. METHODOLOGY: Four thousand nine hundred ninety clinicians from 94 MHA member hospitals/systems responded to a 2018 survey using a brief instrument adapted, in part, from previously validated measures. RESULTS: As hypothesized, job demands were strongly related to burnout, whereas resources were most related to work engagement. Variables from the MHA model explained 40% of variability in burnout and 24% of variability in work engagement. Variables related to burnout with the highest beta weights included having sufficient time for work (-0.266), values alignment with leaders (-0.176), and teamwork efficiency (-0.123), all ps < .001. Variables most associated with engagement included values alignment (0.196), feeling appreciated (0.163), and autonomy (0.093), ps < .001. CONCLUSION: Findings support the basic premises of the proposed conceptual model. Remediable work-life conditions, such as having sufficient time to do the job, values alignment with leadership, teamwork efficiency, feeling appreciated, and clinician autonomy, manifested the strongest associations with burnout and work engagement. PRACTICE IMPLICATIONS: Interventions reducing job demands and strengthening resources such as values alignment, teamwork efficiency, and clinician autonomy are seen as having the greatest potential efficacy.

Harnessing the power of enhanced data for healthcare quality improvement: lessons from a Minnesota Hospital Association Pilot Project.

The imperative to achieve quality improvement and cost-containment goals is driving healthcare organizations to make better use of existing health information. One strategy, the construction of hybrid data sets combining clinical and administrative data, has strong potential to improve the cost-effectiveness of hospital quality reporting processes, improve the accuracy of quality measures and rankings, and strengthen data systems. Through a two-year contract with the Agency for Healthcare Research and Quality, the Minnesota Hospital Association launched a pilot project in 2007 to link hospital clinical information to administrative data. Despite some initial challenges, this project was successful. Results showed that the use of hybrid data allowed for more accurate comparisons of risk-adjusted mortality and risk-adjusted complications across Minnesota hospitals. These increases in accuracy represent an important step toward targeting quality improvement efforts in Minnesota and provide important lessons that are being leveraged through ongoing projects to construct additional enhanced data sets. We explore the implementation challenges experienced during the Minnesota Pilot Project and their implications for hospitals pursuing similar data-enhancement projects. We also highlight the key lessons learned from the pilot project's success.

Admission Laboratory Results to Enhance Prediction Models of Postdischarge Outcomes in Cardiac 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.

Adding Laboratory Data to Hospital Claims Data to Improve Risk Adjustment of Inpatient/30-Day Postdischarge Outcomes.

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.

Promoting collaboration and transparency in patient safety.

BACKGROUND: The Minnesota Alliance for Patient Safety (MAPS) collaborative was founded in 2000 by the Minnesota Hospital Association (MHA), the Minnesota Medical Association, and the Minnesota Department of Health. CREATING A CULTURE OF LEARNING, JUSTICE, AND ACCOUNTABILITY: MAPS made it a priority to make the health care workplace one that encourages learning from adverse events. MAPS is pioneering a statewide model of a "just" culture--one that supports learning yet holds individuals accountable for errors. LEGISLATIVE CHANGES: In 2001, MAPS helped revise the Minnesota peer review law to allow hospitals to share key safety information through electronic databases such as the MHA Patient Safety Registry. The revisions paved the way for the 2003 landmark Minnesota Adverse Health Care Event Reporting Act, which encourages reporting of root cause investigations and steps taken by facilities to prevent recurrence. In 2003 the Patient Safety Registry, an electronic database, was expanded to serve as a confidential clearinghouse for facilities' reporting of adverse events. PATIENT SAFETY TOPICS: MAPS serves as catalyst for developing and disseminating best practices on topics such as health literacy, falls prevention, culture of safety, engaging patients, and consumers' medication tracking. CONCLUSION: The six-year collaborative effort by the many organizations comprising MAPS has led to a transformation in Minnesota's health care safety culture.

Incorporating the Last Four Digits of Social Security Numbers Substantially Improves Linking Patient Data from De-identified Hospital Claims Databases.

OBJECTIVE: Assess algorithms for linking patients across de-identified databases without compromising confidentiality. DATA SOURCES/STUDY SETTING: Hospital discharges from 11 Mayo Clinic hospitals during January 2008-September 2012 (assessment and validation data). Minnesota death certificates and hospital discharges from 2009 to 2012 for entire state (application data). STUDY DESIGN: Cross-sectional assessment of sensitivity and positive predictive value (PPV) for four linking algorithms tested by identifying readmissions and posthospital mortality on the assessment data with application to statewide data. DATA COLLECTION/EXTRACTION METHODS: De-identified claims included patient gender, birthdate, and zip code. Assessment records were matched with institutional sources containing unique identifiers and the last four digits of Social Security number (SSNL4). PRINCIPAL FINDINGS: Gender, birthdate, and five-digit zip code identified readmissions with a sensitivity of 98.0 percent and a PPV of 97.7 percent and identified postdischarge mortality with 84.4 percent sensitivity and 98.9 percent PPV. Inclusion of SSNL4 produced nearly perfect identification of readmissions and deaths. When applied statewide, regions bordering states with unavailable hospital discharge data had lower rates. CONCLUSION: Addition of SSNL4 to administrative data, accompanied by appropriate data use and data release policies, can enable trusted repositories to link data with nearly perfect accuracy without compromising patient confidentiality. States maintaining centralized de-identified databases should add SSNL4 to data specifications.