A concept-wide association study to identify potential risk factors for nonadherence among prevalent users of antihypertensives.

PURPOSE: We sought to determine whether an association study using information contained in clinical notes could identify known and potentially novel risk factors for nonadherence to antihypertensive medications. METHODS: We conducted a retrospective concept-wide association study (CWAS) using clinical notes to identify potential risk factors for medication nonadherence, adjusting for age, sex, race, baseline blood pressure, estimated glomerular filtration rate, and a combined comorbidity score. Participants included Medicare beneficiaries 65 years and older receiving care at the Harvard Vanguard Medical Associates network from 2010-2012 and enrolled in a Medicare Advantage program. Concepts were extracted from clinical notes in the year prior to the index prescription date for each patient. We tested associations with the outcome for 5013 concepts extracted from clinical notes in a derivation cohort (4382 patients) and accounted for multiple hypothesis testing by using a false discovery rate threshold of less than 5% (q < .05). We then confirmed the associations in a validation cohort (3836 patients). Medication nonadherence was defined using a proportion of days covered (PDC) threshold less than 0.8 using pharmacy claims data. RESULTS: We found 415 concepts associated with nonadherence, which we organized into 11 clusters using a hierarchical clustering approach. Volume depletion and overload, assessment of needs at the point of discharge, mood disorders, neurological disorders, complex coordination of care, and documentation of noncompliance were some of the factors associated with nonadherence. CONCLUSIONS: This approach was successful in identifying previously described and potentially new risk factors for antihypertensive nonadherence using the clinical narrative.

The relative benefits of claims and electronic health record data for predicting medication adherence trajectory.

BACKGROUND: Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims. METHODS: In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi-specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group-based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence. RESULTS: Claims were highly predictive of patients in the worst adherence trajectory (C=0.78), but EHR data also provided good predictions (C=0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables. CONCLUSION: EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource-intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available.

Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims and Electronic Health Records.

BACKGROUND: Using electronic health records (EHRs), in addition to claims, to systematically identify patients with factors associated with adverse outcomes (geriatric risk) among older adults can prove beneficial for population health management and clinical service delivery. OBJECTIVE: To define and compare geriatric risk factors derivable from claims, structured EHRs, and unstructured EHRs, and estimate the relationship between geriatric risk factors and health care utilization. RESEARCH DESIGN: We performed a retrospective cohort study of patients enrolled in a Medicare Advantage plan from 2011 to 2013 using both administrative claims and EHRs. We defined 10 individual geriatric risk factors and a summary geriatric risk index based on diagnosed conditions and pattern matching techniques applied to EHR free text. The prevalence of geriatric risk factors was estimated using claims, structured EHRs, and structured and unstructured EHRs combined. The association of geriatric risk index with any occurrence of hospitalizations, emergency department visits, and nursing home visits were estimated using logistic regression adjusted for demographic and comorbidity covariates. RESULTS: The prevalence of geriatric risk factors increased after adding unstructured EHR data to structured EHRs, compared with those derived from structured EHRs alone and claims alone. On the basis of claims, structured EHRs, and structured and unstructured EHRs combined, 12.9%, 15.0%, and 24.6% of the patients had 1 geriatric risk factor, respectively; 3.9%, 4.2%, and 15.8% had ≥2 geriatric risk factors, respectively. Statistically significant association between geriatric risk index and health care utilization was found independent of demographic and comorbidity covariates. For example, based on claims, estimated odds ratios for having 1 and ≥2 geriatric risk factors in year 1 were 1.49 (P<0.001) and 2.62 (P<0.001) in predicting any occurrence of hospitalizations in year 1, and 1.32 (P<0.001) and 1.34 (P=0.003) in predicting any occurrence of hospitalizations in year 2. CONCLUSIONS: The results demonstrate the feasibility and potential of using EHRs and claims for collecting new types of geriatric risk information that could augment the more commonly collected disease information to identify and move upstream the management of high-risk cases among older patients.

Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods.

BACKGROUND: Geriatric syndromes in older adults are associated with adverse outcomes. However, despite being reported in clinical notes, these syndromes are often poorly captured by diagnostic codes in the structured fields of electronic health records (EHRs) or administrative records. OBJECTIVE: We aim to automatically determine if a patient has any geriatric syndromes by mining the free text of associated EHR clinical notes. We assessed which statistical natural language processing (NLP) techniques are most effective. METHODS: We applied conditional random fields (CRFs), a widely used machine learning algorithm, to identify each of 10 geriatric syndrome constructs in a clinical note. We assessed three sets of features and attributes for CRF operations: a base set, enhanced token, and contextual features. We trained the CRF on 3901 manually annotated notes from 85 patients, tuned the CRF on a validation set of 50 patients, and evaluated it on 50 held-out test patients. These notes were from a group of US Medicare patients over 65 years of age enrolled in a Medicare Advantage Health Maintenance Organization and cared for by a large group practice in Massachusetts. RESULTS: A final feature set was formed through comprehensive feature ablation experiments. The final CRF model performed well at patient-level determination (macroaverage F1=0.834, microaverage F1=0.851); however, performance varied by construct. For example, at phrase-partial evaluation, the CRF model worked well on constructs such as absence of fecal control (F1=0.857) and vision impairment (F1=0.798) but poorly on malnutrition (F1=0.155), weight loss (F1=0.394), and severe urinary control issues (F1=0.532). Errors were primarily due to previously unobserved words (ie, out-of-vocabulary) and a lack of context. CONCLUSIONS: This study shows that statistical NLP can be used to identify geriatric syndromes from EHR-extracted clinical notes. This creates new opportunities to identify patients with geriatric syndromes and study their health outcomes.

A self-instructional model to teach systems-based practice and practice-based learning and improvement.

BACKGROUND: When mandated as resident competencies in 1999, systems-based practice (SBP) and practice-based learning and improvement (PBLI) were new concepts to many. OBJECTIVE: To describe and evaluate a 4-week clinical elective (Achieving Competence Today-ACT) to teach residents SBP and PBLI. DESIGN: ACT consisted of a four-week active learning course and follow-up teaching experience, guided and supported by web-based materials. The curriculum included readings, scheduled activities, work products including an improvement project, and weekly meetings with a non-expert preceptor. The evaluation used a before-after cross-comparison of ACT residents and their peers. PARTICIPANTS: Seventy-eight residents and 42 faculty in 18 US Internal Medicine residency programs participated between 2003 and 2005. RESULTS AND MAIN MEASUREMENTS: All residents and faculty preceptors responded to a knowledge test, survey of attitudes, and self-assessment of competency to do 15 tasks related to SBP/PBLI. All measures were normalized to a 100-point scale. Each program's principal investigator (PI) identified aspects of ACT that were most and least effective in enhancing resident learning. ACT residents' gains in knowledge (4.4 on a 100-point scale) and self-assessed competency (11.3) were greater than controls' (-1.9, -8.0), but changes in attitudes were not significantly different. Faculty preceptors' knowledge scores did not change, but their attitudes became more positive (15.8). PIs found a ready-to-use curriculum effective (rated 8.5 on a 10-point scale). CONCLUSIONS: ACT increased residents' knowledge and self-assessment of their own competency and raised faculty's assessment of the importance of residents' learning SBP/PBLI. Faculty content expertise is not required for residents to learn SBP/PBLI.

Population management, systems-based practice, and planned chronic illness care: integrating disease management competencies into primary care to improve composite diabetes quality measures.

The increasing prevalence of chronic illnesses in the United States requires a fundamental redesign of the primary care delivery system's structure and processes in order to meet the changing needs and expectations of patients. Population management, systems-based practice, and planned chronic illness care are 3 potential processes that can be integrated into primary care and are compatible with the Chronic Care Model. In 2003, Harvard Vanguard Medical Associates, a multispecialty ambulatory physician group practice based in Boston, Massachusetts, began implementing all 3 processes across its primary care practices. From 2004 to 2006, the overall diabetes composite quality measures improved from 51% to 58% for screening (HgA1c x 2, low-density lipoprotein, blood pressure in 12 months) and from 13% to 17% for intermediate outcomes (HgA1c

The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification.

OBJECTIVES: To examine the value of unstructured electronic health record (EHR) data (free-text notes) in identifying a set of geriatric syndromes. DESIGN: Retrospective analysis of unstructured EHR notes using a natural language processing (NLP) algorithm. SETTING: Large multispecialty group. PARTICIPANTS: Older adults (N=18,341; average age 75.9, 58.9% female). MEASUREMENTS: We compared the number of geriatric syndrome cases identified using structured claims and structured and unstructured EHR data. We also calculated these rates using a population-level claims database as a reference and identified comparable epidemiological rates in peer-reviewed literature as a benchmark. RESULTS: Using insurance claims data resulted in a geriatric syndrome prevalence ranging from 0.03% for lack of social support to 8.3% for walking difficulty. Using structured EHR data resulted in similar prevalence rates, ranging from 0.03% for malnutrition to 7.85% for walking difficulty. Incorporating unstructured EHR notes, enabled by applying the NLP algorithm, identified considerably higher rates of geriatric syndromes: absence of fecal control (2.1%, 2.3 times as much as structured claims and EHR data combined), decubitus ulcer (1.4%, 1.7 times as much), dementia (6.7%, 1.5 times as much), falls (23.6%, 3.2 times as much), malnutrition (2.5%, 18.0 times as much), lack of social support (29.8%, 455.9 times as much), urinary retention (4.2%, 3.9 times as much), vision impairment (6.2%, 7.4 times as much), weight loss (19.2%, 2.9 as much), and walking difficulty (36.34%, 3.4 as much). The geriatric syndrome rates extracted from structured data were substantially lower than published epidemiological rates, although adding the NLP results considerably closed this gap. CONCLUSION: Claims and structured EHR data give an incomplete picture of burden related to geriatric syndromes. Geriatric syndromes are likely to be missed if unstructured data are not analyzed. Pragmatic NLP algorithms can assist with identifying individuals at high risk of experiencing geriatric syndromes and improving coordination of care for older adults.

An outbreak of group A Streptococcal infection among health care workers.

We describe the nosocomial transmission of group A Streptococcus species (GAS) from a single source patient to 24 health care workers (HCWs). DNA typing revealed that all of the isolates were identical to that of the source patient. The isolates were M type 1, positive for production of nicotine adenine dinucleotidase, and negative for opacity factor, all of which are factors reported to have a higher correlation with invasive disease. The 24 HCWs developed symptoms of pharyngitis < or =4 days after exposure to the source patient. Nosocomial transmission occurred < or =25 h after exposure to the source patient, before the institution of outbreak-control measures. A questionnaire was distributed to HCWs to help identify the factors responsible for the high attack rate among those who were exposed. Invasive GAS disease in a nosocomial setting can be highly transmissible. Rapid identification, early treatment, and adherence to infection-control practices may prevent or control outbreaks of infection.