Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Modeling Antihypertensive Therapeutic Inertia And Intensification To Support Clinical Action Toward Hypertension Control

Background Hypertension is the leading modifiable risk factor for cardiovascular disease and consequent mortality worldwide. In the U.S., more than half of hypertension cases remain uncontrolled, despite availability of effective pharmaceutical treatment options. Evidence suggests that therapeutic inertia, defined as clinician failure to initiate or increase therapy when treatment goals are unmet, is the most influential barrier to improving hypertension control. Substantial rates of therapeutic inertia have been reported in ambulatory primary care settings where hypertension is typically treated and managed. Understanding and overcoming the forces driving therapeutic inertia in hypertension management is a critical strategy to reach population health goals for blood pressure control and cardiovascular disease prevention. Objectives Three embedded studies within this dissertation that include: (1) descriptive and predictive modeling of antihypertensive therapeutic inertia, (2) a model of antihypertensive treatment selection, and (3) a propensity-score matched model of observed reductions in blood pressure after increasing dose or adding new classes of antihypertensive medication using electronic health record (EHR) data generated from real-world clinical practice. Materials and Methods Data for defining and modeling antihypertensive therapeutic inertia comes from five health care organizations; four located in the Southeast and one in the Midwest U.S. EHR data extracted from each system used in these analyses include patient demographic information, diagnoses, procedures, medications, vital signs, and laboratory measurements. Mixed-effects regression, classification trees, and ensemble learning, and propensity-score matching are applied to produce descriptive and predictive models of antihypertensive therapeutic inertia and intensification, treatment selection, and treatment effectiveness. Results For 120,755 patients with hypertension, therapeutic inertia was indicated at 84.1% of 168,222 visits where BP was uncontrolled (\u3e140/\u3e90mmHg). Therapeutic intensification occurred via dose increase of existing medication at 6.6% of visits, and addition of a new medication class at 9.2% of visits with uncontrolled BP. Mixed-effects modeling of patient and clinical variables extracted from the electronic health record accounted for 13.2% of the variance in therapeutic inertia vs. intensification among visits with uncontrolled BP. Gradient boosted classification trees produced the strongest predictive model of therapeutic inertia (test AUC: 0.748). Mixed-effects modeling explained 38.5% of the variance between treatment selection options. Propensity-score matched cases of treatment selection groups found a 1.31 mmHg greater reduction in SBP when a new class of medication was added. Discussion Patient, clinical, and encounter related variables extracted from the EHR did not account for a significant proportion of the observed variance in antihypertensive therapeutic inertia vs. intensification and increasing dose vs. adding a new medication. Consequently, predictive modeling using these variables was limited in performance. However, modeling of the relationship between EHR derived variables and therapeutic inertia/intensification and treatment selection was sufficiently robust to determine the contribution of patient and visit related clinical factors to likelihood of antihypertensive treatment action, and to evaluate the best methods for prediction of hypertension treatment events

Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone

Person-Centered Care Education for Caregivers of Patients With Dementia in Long-Term Care Settings

Person-centered care (PCC) guidelines are being used as a care model to improve health outcomes for residents with impaired cognition. The researcher utilized PCC guidelines to educate caregivers in residential homes to provide care based on the residents’ individualized needs and choices to reduce worsening health conditions and potentially avoidable hospitalizations. The researcher created an educational tool to improve prompt management of health conditions for residents with cognitive impairment and set up a control group (n = 4) and an intervention group (n = 4) to conduct this project. The intervention group received the PCC education guidelines, whereas the control group did not. The researcher utilized Quality of Life in Late-Stage Dementia (QUALID) and Person-Centered Care Assessment Tool (P-CAT) questionnaires to identify caregivers’ perceptions of the person-centeredness and quality of life for residents under their care before and after the PCC education. Caregivers in the intervention group did not show any significant changes in PCC or QUALID scores pre- and post-intervention. In addition, caregivers in the control group had a mean preintervention P-CAT total score (M = 51.00, SD = 4.24) that was significantly higher than the mean post-intervention P-CAT total score (M = 49.50, SD = 4.12). The scores of both groups indicated that their work environments had a high level of PCC before the intervention. Further studies should be done on PCC education in residential care homes to identify the health outcomes of residents with impaired cognition whose caregivers received PCC education and training for 6 months to 1 year

Understanding Falls Risk Screening Practices and Potential for Electronic Health Record Data-Driven Falls Risk Identification in Select West Virginia Primary Care Centers

Unintentional falls among older adults are a complex public health problem both nationally and in West Virginia. Nationally, nearly 40% of community-dwelling adults age 65 and older fall at least once a year, making unintentional falls the leading cause of both fatal and non-fatal injuries among this age group. This problem is especially relevant to West Virginia, which has a population ageing faster on average than the rest of the nation. Identifying falls risk in the primary care setting poses a serious challenge. Currently, the Timed Get-Up-and-Go test is the only recommended screening tool for determining risk. However, nationally this test is completed only 30-37% of the time. Use of electronic health record data as clinical decision support in identifying at-risk patients may help alleviate this problem. However, to date there have been no published studies on using electronic health record data as clinical decision support in the identification of this particular population. This presents opportunity to contribute to the fields of falls prevention and health informatics through novel use of electronic health record data. That stated, this research is designed to: 1) develop an understanding of current falls risk screening practices, facilitators, and barriers to screening in select West Virginia primary care centers; 2) assess the capture of falls risk data and the quality of those data to help facilitate identification of at-risk patients; and 3) build an internally validated model for using electronic health record data for identification of at-risk patients. Through focus group discussions with primary care partners, we find a significant lack of readiness to innovatively use routinely collected data for population health management for falls prevention. The topic of falls risk identification is a rarely discussed topic across these sites, with accompanying low rates of screening and ad-hoc documentation. The need for enhanced team-based care, policy, and procedure surrounding falls is evident. Using de-identified electronic health record data from a sample of West Virginia primary care centers, we find that it is both feasible and worthwhile to repurpose routinely collected data to identify older adult patients at-risk for falls. Among 3,933 patients 65 and older, only 133 patients (3.4%) have an indication in their medical records of falling. Searching the free text data was vital to finding even this low number of patients, as 33.8% were identified using free text searches. Given the focus group findings, underreporting of falls on the part of the patients and missed opportunities to learn of falls due to lack of information sharing across health care service sites are also contributing factors. Similarly, documentation of falls risk assessments were sparse with only 23 patients (0.6%) having documentation of a falls risk assessment in their medical records at some point in the past. As with falls, locating documentation of falls risk assessments was largely dependent on semi-structured and free text data. Current Procedural Terminology coding alone missed 26.1% of all falls risk assessments. Repurposing electronic health record data in a population health framework allows for concurrent examination of primary and secondary falls risk factors in a way which is sensitive to time constraints of the routine office visit, complementary to the movement toward Meaningful Use, while providing opportunity to bolster low screening rates

Multimorbidity Among Adult Primary Health Care Patients In Canada: Examining Multiple Chronic Diseases Using An Electronic Medical Record Database

Introduction: The coexistence of multiple chronic diseases within an individual, also known as multimorbidity, is an ongoing challenge for patients, caregivers and primary health care (PHC) providers. An enhanced understanding of the burden of multimorbidity in Canada is needed. Objectives: This research had two main objectives. Objective One aimed to understand the prevalence of multimorbidity among adult PHC patients, as well as the patterns of unordered and ordered clusters of multiple chronic diseases. Objective Two aimed to determine the natural progression of multimorbidity over time, as well as the patient-, provider- and practice-level predictors of progressing into more complex clinical profiles. Methods: Data were derived from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) electronic medical record (EMR) database. For Objective One, descriptive and computational analyses were conducted and for Objective Two, multilevel survival analyses were conducted to account for clustering. Patients with at least one encounter recorded in their EMR and who were at least 18 years of age at their first encounter were included in the analyses. Chronic disease diagnoses were identified using the International Classification of Diseases, 9th Revision (ICD-9) and a list of 20 chronic disease categories identified patients with multimorbidity. Results: Overall, 53.3% and 33.1% of adult PHC patients were living with at least two and at least three chronic diseases, respectively. Patients with at least two chronic diseases had a mean age of 59.0 years (SD: 17.0), while the majority were female (57.8%) and living in an urban setting (52.2%). Among female patients with multimorbidity, 6,095 unique combinations and 14,911 unique permutations were found. Among male patients with multimorbidity, 4,316 unique combinations and 9,736 unique permutations were detected. The multilevel survival analysis indicated that several patient-level (patient age, patient sex and total number of chronic diseases), provider-level (provider age) and practice-level (EMR type and practice location) variables predicted time until subsequent chronic disease diagnoses. Conclusion: This research explored the prevalence, patterns and natural progression of multimorbidity over time among a large cohort of adult PHC patients. When carefully assessed, these findings will help to create a more nuanced understanding of the burden of multimorbidity