Biomedical concept association and clustering using word embeddings

Indiana University-Purdue University Indianapolis (IUPUI)Biomedical data exists in the form of journal articles, research studies, electronic health records, care guidelines, etc. While text mining and natural language processing tools have been widely employed across various domains, these are just taking off in the healthcare space. A primary hurdle that makes it difficult to build artificial intelligence models that use biomedical data, is the limited amount of labelled data available. Since most models rely on supervised or semi-supervised methods, generating large amounts of pre-processed labelled data that can be used for training purposes becomes extremely costly. Even for datasets that are labelled, the lack of normalization of biomedical concepts further affects the quality of results produced and limits the application to a restricted dataset. This affects reproducibility of the results and techniques across datasets, making it difficult to deploy research solutions to improve healthcare services. The research presented in this thesis focuses on reducing the need to create labels for biomedical text mining by using unsupervised recurrent neural networks. The proposed method utilizes word embeddings to generate vector representations of biomedical concepts based on semantics and context. Experiments with unsupervised clustering of these biomedical concepts show that concepts that are similar to each other are clustered together. While this clustering captures different synonyms of the same concept, it also captures the similarities between various diseases and the symptoms that those diseases are symptomatic of. To test the performance of the concept vectors on corpora of documents, a document vector generation method that utilizes these concept vectors is also proposed. The document vectors thus generated are used as an input to clustering algorithms, and the results show that across multiple corpora, the proposed methods of concept and document vector generation outperform the baselines and provide more meaningful clustering. The applications of this document clustering are huge, especially in the search and retrieval space, providing clinicians, researchers and patients more holistic and comprehensive results than relying on the exclusive term that they search for. At the end, a framework for extracting clinical information that can be mapped to electronic health records from preventive care guidelines is presented. The extracted information can be integrated with the clinical decision support system of an electronic health record. A visualization tool to better understand and observe patient trajectories is also explored. Both these methods have potential to improve the preventive care services provided to patients

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

Dental Value-Based Models and a Proposed Revision of Metrics for New York State's Quality Assurance of Preventive Dental Care

The 2000 Surgeon General report declared dental caries (tooth decay) a "silent epidemic" [1]. Dental caries is preventable, yet it is the most common chronic disease among children and adolescents in the US [2]–[6]. US dental care systems have fallen short of preventing avoidable oral diseases and their consequences, despite enormous dental expenditures. Self-care through oral hygiene, a key health behavior for caries risk reduction, is not practiced nearly as consistently or accurately as dentists and health organizations would like [7],[8]. Evidence-based and cost-effective preventive dental treatments exist but are underutilized. Value-based care (VBC) is an effort to strengthen the quality of dental care, decrease unnecessary expenditure and increase cost-effectiveness through an emphasis on prevention. This paper has three aims. Aim #1 is to understand the current dental VBC landscape through a narrative review. It synthesizes the available literature on dental value-based models and their challenges. Aim #2 discusses the need for quality metrics to help meet VBC goals. It evaluates New York State’s (NYS) quality assurance of dental care among NYS’s Medicaid recipients 0-20 years of age through their Quality Assurance Reporting Requirement (QARR). Aim #3 proposes a revision of metrics and identifies the needed indicators for NYS’s quality assurance of preventive dental care for Medicaid recipients 0-20 years of age. Under Aim #1 Sixty-nine abstracts were reviewed. Forty-six articles met the inclusion criterion and were classified according to four prominent themes: dental metrics, interprofessional collaboration, information technology, and care/case management. Common challenges were identified in dental VBC, including but not limited to, the requirement for interdisciplinary, interoperability; too much involvement of stakeholders; variations in dental treatment modalities and coverage; and challenges in developing appropriate metrics. While initiatives in the peer-reviewed articles leveraged value metrics for the assessment of their programs, no studies among the 46 articles evaluated statewide governmental quality measures’ effectiveness for VBC. Gray literature was therefore utilized for Aim #2. The second aim discusses the need for quality metrics to help meet VBC goals and identifies tested and scalable dental metrics. Healthcare Effectiveness Data Information Set and Dental Quality Alliance were found to be the leading dental metric developers. NYS’s Quality Assurance Reporting Requirement (QARR) indicators were identified as the lead metric set used to evaluate NYS’s Medicaid Managed Care plans and Managed Care Organizations. Several limitations were identified in NYS’s QARR dental metric. It is limited to one dental indicator, it does not measure the quality of dental services and has unintended consequences. Under AIM #3, ten recommended steps are provided for the NYS Department of Health Office of Health Insurance Program (NYS DOH OHIP) to strengthen their quality assurance of preventive dental care among Medicaid recipients 0-20 years of age. The ten steps NYS DOH OHIP can take to develop a dental metric set aimed to assess and improve the utilization of preventive dental services are as follows, 1. Define value from the perspective of the patient 2. Select tested, specific, and measurable metrics 3. Metrics need to be attainable, relevant and anchored within a time frame 4. Metrics need to be appropriate proxies 5. Account for patient characteristics, when feasible 6. Strive for standardized data collection and harmonization 7. Build appropriate information technology infrastructure 8. Reimburse dental providers through alternative payment models 9. Strengthen Provider Accountability by Improving QARR’s Use of Dental Metrics 10. Amend QARR to strengthen quality assurance of preventive dental services A final set of recommendations proposes revisions to NYS’s assessment of preventive dental services through QARR. The recommendations provide technical details on the needed amendments and additions for a more robust metric set to improve NYS’s quality assurance of preventive dental care among NY’s youngest Medicaid recipients. The final set of recommendations which will make system-level changes in dental care delivery and shift NYS towards dental VBC are as follows, Revise QARR’s oral health indicator to measure the percentage of all enrolled children under age 21 who received dental services within the reporting year Replace QARR’s dental screening encounter code (D0190) with comprehensive (D0150) or periodic (D0120) oral examination codes Create a metric for the application of sealants in the erupted, unrestored adult molar teeth of children. Create a metric for the application of topical fluoride at least once every six months on all teeth of children and adolescents under the age of 21 Utilize existing tested and validated preventive dental metrics Raise statewide QARR benchmark metrics annually to encourage higher utilization and success of services Tie performance metrics’ achievement to reimbursement (P4P) Evaluate implemented metrics for their effectiveness in reducing the need for restorative services to help achieve VBC goals among NYS Medicaid recipients 0-20 years of age Release an open comment period to seek stakeholders’ perceptio

The use of electronic narratives records to support the decision-making process in oncology care at private hospitals in Cape Town

Thesis (MTech (Information technology))--Cape Peninsula University of Technology, 2019Electronic narratives are recognised for their significant contribution to healthcare – emphasising that the patient’s narrative should not only be included, but valued. The survival rate of cancer patients in the UK, USA, Italy and Australia are improving, making it necessary to investigate the use of electronic narratives in private oncology centres. This research, conducted in Cape Town, South Africa, started off by critically analysing available scientific information. Subsequently, a gap was identified regarding the use of electronic narratives as a way of acquiring important data from patients – something that is crucial in the treatment process (from the pre-diagnosis to the follow-up), and in decision-making. The lack of narratives in electronic health records (EHRs) could affect the quality of the decision-making process, particularly for chronic non-communicable diseases (NCD); which could result in administering incorrect dosages of medication leading to deterioration of the patient’s health, and in some cases, even death. The purpose of this research was to explore the use of narratives in electronic health records to support the decision-making process by healthcare professionals in private oncology care. The study was qualitative; hence interviews were used for data collection. A purposive sample of eighteen healthcare professionals (oncologists, psychiatrists and general practitioners) was used in this study. The data was then analysed thematically, and the interpretation thereof done subjectively. The key findings of this study indicate that electronic health records are used considerably in private oncology care due to benefits such as real-time access to information and easy back-up. Healthcare professionals acknowledge that narratives are present in oncology care, and mainly used in the diagnosis phase. These narratives are mostly in note format (hand-written on paper). These written notes are then later recorded into the patient’s electronic health record which, in many cases, results in the omission of important information, because not everything the patient said is transcribed into medical jargon. The current system in private oncology care does not support electronic narratives even though healthcare professionals express an interest in using this. The findings further suggest that to successfully implement electronic narratives, there are basic prerequisites such as a computer or tablet, recording devices and software. Furthermore, the findings show that electronic narratives are often not used due to limited knowledge, lack of interest, specific cultural practices, and the fear of change. To alter and positively transform healthcare professionals’ and patients’ views of electronic narratives, the researcher recommends educating healthcare professionals about the value of patients’ narratives. In other words, providing training is crucial as narratives contain values that aid constructive decision-making. Furthermore, since narratives involve patients, extending training to the patients will be beneficial. The findings of this study contribute to the current literature on electronic health records and narratives in private oncology care of South Africa

Impact of implementing a computerised quality improvement intervention in primary healthcare

Health systems worldwide experience large evidence practice gaps with underuse of proven therapies, overuse of inappropriate treatments and misuse of treatments due to medical error. Quality improvement (QI) initiatives have been shown to overcome some of these gaps. Computerised interventions, in particular, are potential enablers to improving system performance. However, implementation of these interventions into routine practice has resulted in mixed outcomes and those that have been successfully integrated into routine practice are difficult to sustain. The objective of this thesis is to understand how a multifaceted, computerised QI intervention for cardiovascular disease (CVD) prevention and management was implemented in Australian general practices and Aboriginal Community Controlled Health Services and assess the implications for scale-up of the intervention. The intervention was implemented as part of a large cluster-randomised controlled trial, the TORPEDO (Treatment of Cardiovascular Risk using Electronic Decision Support) study. The intervention was associated with improved guideline recommended cardiovascular risk factor screening rates but had mixed impact on improving medication prescribing rates. In this thesis, I designed a multimethod process and economic evaluation of the TORPEDO trial. The aims were to: i. Develop a theory-informed logic model to assist in the design of the overall evaluation to address study aims (Chapter 3). ii. Conduct a post-trial audit to quantify changes in cardiovascular risk factor screening and prescribing to high risk patients over an 18-month post-trial period and understand the impact of the intervention outside of a research trial setting (Chapter 4). vi iii. Use normalisation process theory to identify the underlying mechanisms by which the intervention did and did not have an impact on trial outcomes (Chapter 5). iv. Use video ethnography to explore how the intervention was used and cardiovascular risk communicated between patients and healthcare providers (Chapter 6). v. Conduct an economic evaluation to inform policy makers for delivering the intervention at scale through Primary Health Networks in New South Wales (Chapter 7). vi. Use a new theory to explain the factors that drove adoption and non-adoption of the intervention and assess what modifications may be needed to promote spread and scale-up (Chapter 8). I found variable outcomes during the post-trial period with a plateauing of improvements in guideline recommended screening practices but an ongoing improvement in prescribing to high risk patients. The group that continued to have the most benefit was patients at high CVD risk who were not receiving recommended medications at baseline. The delay in prescribing recommended medication suggests healthcare providers adopt a cautious approach when introducing new treatments. Six intervention primary healthcare services participated as case studies for the process evaluation. Qualitative and quantitative data sources were combined at each primary healthcare service to enable a detailed examination of intervention implementation from multiple perspectives. The process evaluation identified the complex interaction between several underlying mechanisms that influenced the implementation processes and explained the mixed trial outcomes: (1) organisational mission; (2) leadership; (3) the role of teams; (4) technical competence and dependability of the software tools. Further, there were different ‘active ingredients’ vii necessary during the initial implementation compared to those needed to sustain use of the intervention. In the video ethnography and post-consultation patient interviews, important insights were gained into how the intervention was used, and its interpretation by the doctor and patient. Through ethnographic accounts, the doctor’s communication of cardiovascular risk was not sufficient in engaging patients and having them act upon their high-risk status; effective communication required interactions be assessed, discussed and negotiated. The economic evaluation identified the cost implications of implementing the intervention as part of a Primary Health Network program in the state of New South Wales, Australia; and modelled data looked at the impact of small but statistically significant reductions in clinical risk factors based on the trial data. When scaled to a larger population the intervention has potential to prevent major CVD events at under AU$50,000 per CVD event averted largely due to the low costs of implementing the intervention. However, the clinical risk factor reductions were small and a stronger case for investment would be made if the effects sizes could be enhanced and sustained over time. The findings from chapters 4-6 provide insight into the intricacy of the barriers influencing implementation processes and adoption of the intervention. Taken together, these studies provide a detailed explanation of the processes that may be required to implement such an intervention at scale and the factors that might influence its impact and sustainability. The findings are expected to assist policy makers, administrators and health professionals in developing multiple interdependent QI strategies at the organisational, provider and consumer levels to improve primary healthcare system performance for cardiovascular disease management and prevention

Utilizing Electronic Dental Record Data to Track Periodontal Disease Change

Indiana University-Purdue University Indianapolis (IUPUI)Periodontal disease (PD) affects 42% of US population resulting in compromised quality of life, the potential for tooth loss and influence on overall health. Despite significant understanding of PD etiology, limited longitudinal studies have investigated PD change in response to various treatments. A major barrier is the difficulty of conducting randomized controlled trials with adequate numbers of patients over a longer time. Electronic dental record (EDR) data offer the opportunity to study outcomes following various periodontal treatments. However, using EDR data for research has challenges including quality and missing data. In this dissertation, I studied a cohort of patients with PD from EDR to monitor their disease status over time. I studied retrospectively 28,908 patients who received comprehensive oral evaluation at the Indiana University School of Dentistry between January 1st-2009 and December 31st-2014. Using natural language processing and automated approaches, we 1) determined PD diagnoses from periodontal charting based on case definitions for surveillance studies, 2) extracted clinician-recorded diagnoses from clinical notes, 3) determined the number of patients with disease improvement or progression over time from EDR data. We found 100% completeness for age, sex; 72% for race; 80% for periodontal charting findings; and 47% for clinician-recorded diagnoses. The number of visits ranged from 1-14 with an average of two visits. From diagnoses obtained from findings, 37% of patients had gingivitis, 55% had moderate periodontitis, and 28% had severe periodontitis. In clinician-recorded diagnoses, 50% patients had gingivitis, 18% had mild, 14% had moderate, and 4% had severe periodontitis. The concordance between periodontal charting-generated and clinician-recorded diagnoses was 47%. The results indicate that case definitions for PD are underestimating gingivitis and overestimating the prevalence of periodontitis. Expert review of findings identified clinicians relying on visual assessment and radiographic findings in addition to the case definition criteria to document PD diagnosis.2021-08-1

An Evaluation of Computational Methods to Support the Clinical Management of Chronic Disease Populations

Innovative primary care models that deliver comprehensive primary care to address medical and social needs are an established means of improving health outcomes and reducing healthcare costs among persons living with chronic disease. Care management is one such approach that requires providers to monitor their respective patient panels and intervene on patients requiring care. Health information technology (IT) has been established as a critical component of care management and similar care models. While there exist a plethora of health IT systems for facilitating primary care, there is limited research on their ability to support care management and its emphasis on monitoring panels of patients with complex needs. In this dissertation, I advance the understanding of how computational methods can better support clinicians delivering care management, and use the management of human immunodeficiency virus (HIV) as an example scenario of use. The research described herein is segmented into 3 aims; the first was to understand the processes and barriers associated with care management and assess whether existing IT can support clinicians in this domain. The second and third aim focused on informing potential solutions to the technological shortcomings identified in the first aim. In the studies of the first aim, I conducted interviews and observations in two HIV primary care programs and analyzed the data generated to create a conceptual framework of population monitoring and identify challenges faced by clinicians in delivering care management. In the studies of the second aim, I used computational methods to advance the science of extracting from the patient record social and behavioral determinants of health (SBDH), which are not easily accessible to clinicians and represent an important barrier to care management. In the third aim, I conducted a controlled experimental evaluation to assess whether data visualization can improve clinician’s ability to maintain awareness of their patient panels

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe