Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup.

Over 78 million people will suffer from dementia by 2030, emphasizing the need for early identification of patients with mild cognitive impairment (MCI) at risk, and personalized clinical evaluation steps to diagnose potentially reversible causes. Here, we leverage real-world electronic health records in the observational medical outcomes partnership (OMOP) data model to develop machine learning models to predict MCI up to a year in advance of recorded diagnosis. Our experimental results with logistic regression, random forest, and xgboost models trained and evaluated on more than 531K patient visits show random forest model can predict MCI onset with ROC-AUC of 68.2±0.7. We identify the clinical factors mentioned in clinician notes that are most predictive of MCI. Using similar association mining techniques, we develop a data-driven list of clinical procedures commonly ordered in the workup of MCI cases, that could be used as a basis for guidelines and clinical order set templates.

Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs.

BACKGROUND: We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. OBJECTIVE: To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. METHODS: We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF. RESULTS: The CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF. CONCLUSIONS: The CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements.

Implementing effective hypertension quality improvement strategies: barriers and potential solutions.

Many quality improvement strategies have focused on improving blood pressure control, and these strategies can target the patient, the provider, and/or the system. Strategies that seem to have the biggest effect on blood pressure outcomes are team change, patient education, facilitated relay of clinical information, and promotion of self-management. Barriers to effective blood pressure control can affect the patient, the physician, the system, and/or "cues to action."We review the barriers to achieving blood pressure control and describe current and potential creative strategies for optimizing blood pressure control. These include home-based disease management, combined patient and provider education, and automatic decision support systems. Future research must address which components of quality improvement interventions are most successful in achieving blood pressure control.

Quality improvement strategies for hypertension management: a systematic review.

BACKGROUND: Care remains suboptimal for many patients with hypertension. PURPOSE: The purpose of this study was to assess the effectiveness of quality improvement (QI) strategies in lowering blood pressure. DATA SOURCES: MEDLINE, Cochrane databases, and article bibliographies were searched for this study. STUDY SELECTION: Trials, controlled before-after studies, and interrupted time series evaluating QI interventions targeting hypertension control and reporting blood pressure outcomes were studied. DATA EXTRACTION: Two reviewers abstracted data and classified QI strategies into categories: provider education, provider reminders, facilitated relay of clinical information, patient education, self-management, patient reminders, audit and feedback, team change, or financial incentives were extracted. DATA SYNTHESIS: Forty-four articles reporting 57 comparisons underwent quantitative analysis. Patients in the intervention groups experienced median reductions in systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were 4.5 mm Hg (interquartile range [IQR]: 1.5 to 11.0) and 2.1 mm Hg (IQR: -0.2 to 5.0) greater than observed for control patients. Median increases in the percentage of individuals achieving target goals for SBP and DBP were 16.2% (IQR: 10.3 to 32.2) and 6.0% (IQR: 1.5 to 17.5). Interventions that included team change as a QI strategy were associated with the largest reductions in blood pressure outcomes. All team change studies included assignment of some responsibilities to a health professional other than the patient's physician. LIMITATIONS: Not all QI strategies have been assessed equally, which limits the power to compare differences in effects between strategies. CONCLUSION: QI strategies are associated with improved hypertension control. A focus on hypertension by someone in addition to the patient's physician was associated with substantial improvement. Future research should examine the contributions of individual QI strategies and their relative costs.