Machine learning approaches to identifying social determinants of health in electronic health record clinical notes

Social determinants of health (SDH) represent the complex set of circumstances in which individuals are born, or with which they live, that impact health. Relatively little attention has been given to processes needed to extract SDH data from electronic health records. Despite their importance, SDH data in the EHR remains sparse, typically collected only in clinical notes and thus largely unavailable for clinical decision making. I focus on developing and validating more efficient information extraction approaches to identifying and classifying SDH in clinical notes. In this dissertation, I have three goals: First, I develop a word embedding model to expand SDH terminology in the context of identifying SDH clinical text. Second, I examine the effectiveness of different machine learning algorithms and a neural network model to classify the SDH characteristics financial resource strain and poor social support. Third, I compare the highest performing approaches to simpler text mining techniques and evaluate the models based on performance, cost, and generalizability in the task of classifying SDH in two distinct data sources.Doctor of Philosoph

Interventions to address the social determinants of health in primary care settings

Social determinants of health (SDOH) are conditions in the environment of individuals’ lives that impact their health, such as where they live, their socioeconomic status, and their access to education. These factors contribute meaningfully to health and can explain a significant portion of disease burden, but they have not traditionally been addressed within healthcare settings. However, a growing understanding of the negative health outcomes associated with SDOH factors has led some health systems and providers to implement interventions within healthcare settings in order to address these issues in patients’ lives. The purpose of this research was to produce a critical literature synthesis of interventions to address the SDOH in primary care settings. While this topic is being focused on more by health systems and providers, there was a need for a comprehensive review of interventions that are being implemented specifically within primary care. This research is significant to public health because intervention to address the SDOH in patients’ lives and communities is essential to accomplish the public health aims of disease prevention and health promotion. A literature search was conducted within the PubMed and Scopus databases and 11 relevant interventions were reviewed. These interventions covered the SDOH-related factors of food insecurity, transportation, unmet legal needs, and interventions that were designed to address multiple factors at once. Interventions fell into two main types: those that involved the direct provision of a good or service and those that involved patient consultation with an individual trained to help them meet their SDOH needs. Based on the interventions reviewed, recommendations for future practice in primary care are discussed. These recommendations include collocating staff who specialize in the SDOH in the primary care office, creating strong relationships between primary care practices and community partners, and that primary care providers engage in advocacy to address the SDOH at structural levels

Stat Med

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.20192021-03-22T00:00:00ZT42 OH008455/OH/NIOSH CDC HHS/United StatesP30 CA046592/CA/NCI NIH HHS/United StatesMC_QA137853/MRC_/Medical Research Council/United KingdomMC_PC_12028/MRC_/Medical Research Council/United KingdomMC_PC_17228/MRC_/Medical Research Council/United Kingdom31859414PMC79838091097

Social and Behavioral Domains in Acute Care Electronic Health Records: Barriers, Facilitators, Relevance, and Value.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

An Evaluation of the Use of a Clinical Research Data Warehouse and I2b2 Infrastructure to Facilitate Replication of Research

Replication of clinical research is requisite for forming effective clinical decisions and guidelines. While rerunning a clinical trial may be unethical and prohibitively expensive, the adoption of EHRs and the infrastructure for distributed research networks provide access to clinical data for observational and retrospective studies. Herein I demonstrate a means of using these tools to validate existing results and extend the findings to novel populations. I describe the process of evaluating published risk models as well as local data and infrastructure to assess the replicability of the study. I use an example of a risk model unable to be replicated as well as a study of in-hospital mortality risk I replicated using UNMC’s clinical research data warehouse. In these examples and other studies we have participated in, some elements are commonly missing or under-developed. One such missing element is a consistent and computable phenotype for pregnancy status based on data recorded in the EHR. I survey local clinical data and identify a number of variables correlated with pregnancy as well as demonstrate the data required to identify the temporal bounds of a pregnancy episode. Next, another common obstacle to replicating risk models is the necessity of linking to alternative data sources while maintaining data in a de-identified database. I demonstrate a pipeline for linking clinical data to socioeconomic variables and indices obtained from the American Community Survey (ACS). While these data are location-based, I provide a method for storing them in a HIPAA compliant fashion so as not to identify a patient’s location. While full and efficient replication of all clinical studies is still a future goal, the demonstration of replication as well as beginning the development of a computable phenotype for pregnancy and the incorporation of location based data in a de-identified data warehouse demonstrate how the EHR data and a research infrastructure may be used to facilitate this effort

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

Considering the impact of social risk screening and referral interventions on adults in the safety-net: a mixed methods approach to health system perspectives

The United States’ healthcare system has focused on identifying determinants of negative health outcomes through the standardized assessment of unmet social needs, termed as ‘social risk screenings.’ Prior research has established different health sector stakeholders’ perceptions regarding the acceptability and utility of these screenings, but there is limited evidence regarding the impact of social risk screening on patient health outcomes. Evidence is especially limited on the impact of social risk screening on persons with mental health or cancer diagnoses, despite high levels of reported unmet social needs in these populations. Additionally, an important consideration in the development and use of social risk screens lies in the potential disconnect between the conceptualization of social needs from the provider versus patient perspectives – a disconnect that may be driven by the ‘medicalization’ of the complex contextual factors that shape one’s social determinants of health. The goal of this dissertation is to generate evidence on the experience of clinical providers charged with administering and responding to social risk screenings, how screenings are associated with service use for patients with mental health needs, and patients’ own experiences of and reactions to being asked about social needs and the resultant impacts on the patient-provider interactive relationship. This dissertation contains three chapters that explore the impact of social risk screening on the adult safety net from multiple health sector perspectives. The first study, Patient Navigator and Clinical Team Perceptions on Addressing Unmet Social Needs: Results from a Breast Cancer Patient Navigation Intervention Study, utilizes primary data collection to characterize how patient navigators and clinical teams seek to screen and address unmet social needs for their patients via a social risk screening and referral intervention embedded in breast cancer care sites across Boston, MA. The second study, The Association between Social Needs Screening and Health Care Utilization Among Adults with Mental Health Needs in the Safety-Net, utilizes secondary data analysis of patient electronic health record data to examine the association of social risk screening on healthcare utilization for patients with mental health needs. The third study, Patient Perceptions and Consideration of Unmet Social Needs whilst Accessing Medical Services: A Qualitative Study utilizes primary data collection to understand the ambulatory care patient experience of responding to social risk screens, how patients prioritize different unmet social needs in light of seeking care, and the impact of social risk screening on the patient-provider relationship