J Biomed Inform

We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.CC999999/ImCDC/Intramural CDC HHS/United States2019-11-20T00:00:00Z28729030PMC6864736694

Comparison of the expressed experiences of survivors of childhood medulloblastoma with measures of health and quality of life, and with issues identified in consultations

When the study was planned, the Young Oncology Unit at The Christie housed both the adult and paediatric follow-up clinics for patients who have been diagnosed with a tumour of the central nervous system (CNS) in childhood and adolescence or young adult life. Over the past 10 years, the paediatric clinic had developed a true multi-disciplinary team function with input from paediatric oncology, clinical oncology, teenage and young adult oncology, neurosurgery, social work, physiotherapy and clinical psychology. In contrast, the adult clinic (for follow-up of patients diagnosed in childhood and also in late teenage or early adult life) had solely medical input. The clinic for the follow-up of adult survivors of childhood CNS tumours at The Christie had significantly less multidisciplinary team involvement than its paediatric counterpart, and it was postulated that adult patients’ needs might not be addressed as fully by the current provision. This study was designed to develop a process to capture information systematically about patients’ problems in the domains of medical, physical, psychological and social wellbeing in order to compare the extent to which these were recognised and supported in the context of the adult and paediatric clinics. Ultimately, this information was expected to inform planning for improvements in survivorship support in both clinics

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