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

Using de-identified electronic health records to research mental health supported housing services: A feasibility study

BACKGROUND: Mental health supported housing services are a key component in the rehabilitation of people with severe and complex needs. They are implemented widely in the UK and other deinstitutionalised countries but there have been few empirical studies of their effectiveness due to the logistic challenges and costs of standard research methods. The Clinical Record Interactive Search (CRIS) tool, developed to de-identify and interrogate routinely recorded electronic health records, may provide an alternative to evaluate supported housing services. METHODS: The feasibility of using the Camden and Islington NHS Foundation Trust CRIS database to identify a sample of users of mental health supported accommodation services. Two approaches to data interrogation and case identification were compared; using structured fields indicating individual's accommodation status, and iterative development of free text searches of clinical notes referencing supported housing. The data used were recorded over a 10-year-period (01-January-2008 to 31-December-2017). RESULTS: Both approaches were carried out by one full-time researcher over four weeks (150 hours). Two structured fields indicating accommodation status were found, 2,140 individuals had a value in at least one of the fields representative of supported accommodation. The free text search of clinical notes returned 21,103 records pertaining to 1,105 individuals. A manual review of 10% of the notes indicated an estimated 733 of these individuals had used a supported housing service, a positive predictive value of 66.4%. Over two-thirds of the individuals returned in the free text search (768/1,105, 69.5%) were identified via the structured fields approach. Although the estimated positive predictive value was relatively high, a substantial proportion of the individuals appearing only in the free text search (337/1,105, 30.5%) are likely to be false positives. CONCLUSIONS: It is feasible and requires minimal resources to use de-identified electronic health record search tools to identify large samples of users of mental health supported housing using structured and free text fields. Further work is needed to establish the availability and completion of variables relevant to specific clinical research questions in order to fully assess the utility of electronic health records in evaluating the effectiveness of these services

For the greater good? Patient and public attitudes to use of medical free text data in research

Objectives: Electronic health records (EHRs) contain rich information for understanding health conditions and their treatment. A large proportion of clinical information in EHRs is stored in narrative free text. This text is currently under-utilised due to privacy concerns, as it is harder to remove patient identifiers from text than from structured data. Automated de-identification of clinical text is now possible using heuristic or machine-learning-based systems. We conducted a review of the literature on patient and public understanding and attitudes towards the use of patients’ medical data for research, particularly seeking views on free text. The aim was to inform and develop a governance framework for the de-identification and use of medical free text for research, and to instigate a wider discussion on the topic. Approach: We undertook a systematic search in Web of Science and ScienceDirect with terms such as “public attitudes” and “electronic health records”. 3480 results were sifted by title, abstract and full text. Forty-two articles were retained for review, these reported on studies of patient and public perceptions, understanding and attitudes towards the use of patients’ medical data in research. Results: Research participants were positively inclined towards information in records being used in research “for the greater good”. However, no clear patterns by age, ethnicity, education level or SES emerged as to who was more favourable to data use. Participants generally trusted health care professionals and public sector researchers with de-identified medical data, whereas government health agencies and commercial entities were not trusted. No explicitly feared harms associated with data use were articulated. However the general objections appeared to be a dislike of personal data being exploited for commercial gain, and a dislike of personal data being moved around and used without personal knowledge or consent. Notably the use of EHR medical text for research did not emerge as a specific patient/public concern. De-identification was important to participants but text was not identified as a distinct privacy risk. Conclusion: This review demonstrates that transparency about data usage, and working “for the greater good” rather than financial gain, appear to be the most important public concerns to be addressed when using patients’ medical data. Governance frameworks for using EHRs must now be enhanced to provide for the use of medical text. This will involve informing both regulators and the public about the current capabilities of automated de-identification, and developing other assurances to safeguard patients’ privacy

Clinical records anonymisation and text extraction (CRATE): an open-source software system.

BACKGROUND: Electronic medical records contain information of value for research, but contain identifiable and often highly sensitive confidential information. Patient-identifiable information cannot in general be shared outside clinical care teams without explicit consent, but anonymisation/de-identification allows research uses of clinical data without explicit consent. RESULTS: This article presents CRATE (Clinical Records Anonymisation and Text Extraction), an open-source software system with separable functions: (1) it anonymises or de-identifies arbitrary relational databases, with sensitivity and precision similar to previous comparable systems; (2) it uses public secure cryptographic methods to map patient identifiers to research identifiers (pseudonyms); (3) it connects relational databases to external tools for natural language processing; (4) it provides a web front end for research and administrative functions; and (5) it supports a specific model through which patients may consent to be contacted about research. CONCLUSIONS: Creation and management of a research database from sensitive clinical records with secure pseudonym generation, full-text indexing, and a consent-to-contact process is possible and practical using entirely free and open-source software.The project was funded in part by the UK National Institute of Health Research Cambridge Biomedical Research Centre. The work was conducted within the Behavioural and Clinical Neuroscience Institute, University of Cambridge, supported by the Wellcome Trust and the UK Medical Research Council