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Telehealth services for patient visits have substantially surged during the COVID-19 pandemic. Thus, there is increased importance and demand for high-quality telehealth clinical documentation. However, little is known about how clinical data documentation is collected and the quality of data items included. This study aimed to identify the current state of and gaps in documentation and develop a best practice strategy for telehealth record documentation. Data were collected from January to February 2021 via a self-designed questionnaire for administrators and managers from physicians’ offices and mental health facilities, resulting in 76 valid responses. Survey items included health organization demographic information, use of telehealth policies and procedures, and clinical documentation for telehealth patient visits. Findings from this study can be used to assist government, policymakers, and healthcare organizations in developing best practices in telehealth usage and clinical documentation improvement strategies.

The COVID-19 pandemic has increased the emphasis on population health, therefore potentially amplifying demand for healthcare workforce professionals in this area. There is an urgent need to explore and define the roles of health information management (HIM) professionals in the population health workforce. This study sought to identify the skill sets and qualifications needed, and HIM education alignment with skills necessary for HIM professionals entering the population health workforce. An intentionally broad internet search of job postings was conducted to determine skills in population health. Population health-related job descriptions and qualification requirements were abstracted and analyzed using ATLAS.ti. Three common job categories were identified: management, analytics, and coding. Skill set requirements included soft skills, problem solving, project management, research, and data analysis. The study results identified HIM educational alignment and found that HIM professionals are generally a good fit to meet the increased need in the population health workforce.

BACKGROUND: During the COVID-19 pandemic, the use of telehealth for patient visits grew rapidly and served an important role as a valuable and necessary resource. Although clinical documentation is critical for telehealth patient visits, there is limited information about how healthcare facilities manage telehealth patient visit documentation, technology used for telehealth visits, and challenges encountered with telehealth patient visit documentation. This study aimed to assess the use of telehealth during the pandemic, the quality of clinical documentation in telehealth practice and to identify challenges and issues encountered with telehealth patient visits in order to develop a strategy for best practices for telehealth documentation and data management. METHODS: Data were collected for this cross-sectional study in January-February 2021 via a self-designed survey of administrators/managers from physicians’ offices and mental health facilities. Survey questions included four categories: health organization demographic information; telehealth visits; clinical documentation for telehealth visit; and challenges and barriers related to telehealth documentation technology use. RESULTS: Of 76 respondents, more than half (62%) of the healthcare facilities started using telehealth for patient visits within one year of the onset of the COVID-19 pandemic, with 94% of respondents indicating an increased use of telehealth for patient visits since the pandemic. The most common types of telehealth patient care provided during the pandemic included pediatrics, primary care, cardiology, and women’s health. The most consistent data documentation of telehealth visits included: date of service, patient identification number, communication methods, patient informed consent, diagnosis and impression, evaluation results, and recommendations. The telehealth visit data was most commonly used for patient care and clinical practice, billing and reimbursement, quality improvement and patient satisfaction, and administrative planning. The top barriers to telehealth use by the healthcare professionals included patient challenges with telehealth services, such as inequities in quality of technology, lack of patient understanding, and lack of patient satisfaction; this was followed by frustration with constant updates of telehealth guidelines and procedures, understanding required telehealth documentation for reimbursement purposes, payer denial for telehealth visits, and legal and risk issues. CONCLUSIONS: Findings from this study can assist government entities, policymakers, and healthcare organizations in developing and advocating best practices in telehealth usage and clinical documentation improvement strategies.

Background Many dynamics in the relationship among military service-related disabilities, health care benefits, mental health disorders, and post-deployment homelessness among US Veterans are not well understood. Objectives Determine whether Veterans with a disability-related discharge from military service are at higher risk for homelessness, whether Veterans Health Administration (VHA) service-connected disability benefits mitigates that risk, and whether risks associated with discharge type, service-connected disability, or the interaction between them vary as a function of mental health disorders. Methods Retrospective cohort study of 364,997 Veterans with a disability-related or routine discharge and initial VHA encounter between 2005 and 2013. Logistic regression and survival analyses were used to estimate homelessness risk as a function of discharge status, mental health disorders, and receipt of VHA disability benefits. Results Disability-discharged Veterans had higher rates of homelessness compared to routine discharges (15.1 verses 9.1 per 1000 person-years at risk). At the time of the first VHA encounter, mental health disorders were associated with differentially greater risk for homelessness among Veterans with a disability discharge relative to those with a routine discharge. During the first year of VHA service usage, higher levels of disability benefits were protective against homelessness among routinely-discharged Veterans, but not among disability-discharged Veterans. By 5-years, disability discharge was a risk factor for homelessness (AOR = 1.30). Conclusions In the long-term, disability discharge is an independent risk factor for homelessness. While VHA disability benefits help mitigate homelessness risk among routinely-discharged Veterans during the early reintegration period, they may not offer sufficient protection for disability-discharged Veterans.

BACKGROUND: The prevalence of esophageal atresia (EA) has been shown to vary across different geographical settings. Investigation of geographical differences may provide an insight into the underlying etiology of EA. METHODS: The study population comprised infants diagnosed with EA during 1998 to 2007 from 18 of the 46 birth defects surveillance programs, members of the International Clearinghouse for Birth Defects Surveillance and Research. Total prevalence per 10,000 births for EA was defined as the total number of cases in live births, stillbirths, and elective termination of pregnancy for fetal anomaly (ETOPFA) divided by the total number of all births in the population. RESULTS: Among the participating programs, a total of 2943 cases of EA were diagnosed with an average prevalence of 2.44 (95% confidence interval [CI], 2.352.53) per 10,000 births, ranging between 1.77 and 3.68 per 10,000 births. Of all infants diagnosed with EA, 2761 (93.8%) were live births, 82 (2.8%) stillbirths, 89 (3.0%) ETOPFA, and 11 (0.4%) had unknown outcomes. The majority of cases (2020, 68.6%), had a reported EA with fistula, 749 (25.5%) were without fistula, and 174 (5.9%) were registered with an unspecified code. CONCLUSIONS: On average, EA affected 1 in 4099 births (95% CI, 1 in 39544251 births) with prevalence varying across different geographical settings, but relatively consistent over time and comparable between surveillance programs. Findings suggest that differences in the prevalence observed among programs are likely to be attributable to variability in population ethnic compositions or issues in reporting or registration procedures of EA, rather than a real risk occurrence difference. Birth Defects Research (Part A), 2012. (c) 2012 Wiley Periodicals, Inc.

BACKGROUND Chronic multisymptom illness (CMI) may be more prevalent among female Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn (OEF/OIF/OND) deployed Veterans due to deployment-related experiences. OBJECTIVES To investigate CMI-related diagnoses among female OEF/OIF/OND Veterans. RESEARCH DESIGN We estimated the prevalence of the International Classification of Disease-9th edition-Clinical Modification coded CMI-related diagnoses of chronic fatigue syndrome, fibromyalgia (FM), and irritable bowel syndrome (IBS) among female OEF/OIF/OND Veterans with Veterans Health Administration (VHA) visits, FY2002-2012 (n=78,435). We described the characteristics of female Veterans with and without CMI-related diagnoses and VHA settings of first CMI-related diagnoses. RESULTS The prevalence of CMI-related diagnoses among female OEF/OIF/OND Veterans was 6397 (8.2%), over twice as high as the prevalence 95,424 (3.9%) among the totality of female Veterans currently accessing VHA (P

Researchers at the U.S. Department of Veterans Affairs (VA) have used administrative criteria to identify homelessness among U.S. Veterans. Our objective was to explore the use of these codes in VA health care facilities. We examined VA health records (2002-2012) of Veterans recently separated from the military and identified as homeless using VA conventional identification criteria (ICD-9-CM code V60.0, VA specific codes for homeless services), plus closely allied V60 codes indicating housing instability. Logistic regression analyses examined differences between Veterans who received these codes. Health care services and co-morbidities were analyzed in the 90 days post-identification of homelessness. VA conventional criteria identified 21,021 homeless Veterans from Operations Enduring Freedom, Iraqi Freedom, and New Dawn (rate 2.5%). Adding allied V60 codes increased that to 31,260 (rate 3.3%). While certain demographic differences were noted, Veterans identified as homeless using conventional or allied codes were similar with regards to utilization of homeless, mental health, and substance abuse services, as well as co-morbidities. Differences were noted in the pattern of usage of homelessness-related diagnostic codes in VA facilities nation-wide. Creating an official VA case definition for homelessness, which would include additional ICD-9-CM and other administrative codes for VA homeless services, would likely allow improved identification of homeless and at-risk Veterans. This also presents an opportunity for encouraging uniformity in applying these codes in VA facilities nationwide as well as in other large health care organizations.

Patients report their symptoms and subjective experiences in their own words. These expressions may be clinically meaningful yet are difficult to capture using automated methods. We annotated subjective symptom expressions in 750 clinical notes from the Veterans Affairs EHR. Within each document, subjective symptom expressions were compared to mentions of symptoms in clinical terms and to the assigned ICD-9-CM codes for the encounter. A total of 543 subjective symptom expressions were identified, of which 66.5% were categorized as mental/behavioral experiences and 33.5% somatic experiences. Only two subjective expressions were coded using ICD-9-CM. Subjective expressions were restated in semantically related clinical terms in 246 (45.3%) instances. Nearly one third (31%) of subjective expressions were not coded or restated in standard terminology. The results highlight the diversity of symptom descriptions and the opportunities to further develop natural language processing to extract symptom expressions that are unobtainable by other automated methods.

Objective To highlight the importance of templates in extracting surveillance data from the free text of electronic medical records using natural language processing (NLP) techniques. Introduction The main stay of recording patient data is the free text of electronic medical records (EMR). While stating the chief complaint and history of presenting illness in the patients ‘own words’, the rest of the electronic note is written by the provider in their words. Providers often use boiler-plate templates from EMR pull-downs to document information on the patient in the form of checklists, check boxes, yes/no and free text responses to questions. When these templates are used for recording symptoms, demographic information or medical, social or travel history, they represent an important source of surveillance data [1]. There is a dearth of literature on the use of natural language processing in extracting data from templates in the EMR. Methods A corpus of 1000 free text medical notes from the VA integrated electronic medical record (CPRS) was reviewed to identify commonly used templates. Of these, 500 were enriched for the surveillance domain of interest for this project (homelessness). The other 500 were randomly sampled from a large corpus of electronic notes. An NLP algorithm was developed to extract concepts related to our target surveillance domain. A manual review of the notes was performed by three human reviewers to generate a document-level reference standard that classified this set of documents as either demonstrating evidence of homelessness (H) or not (NH). A rule-based NLP algorithm was developed that used a combination of key word searches and negation based on an extensive lexicon of terms developed for this purpose. A random sample of 50 documents each of H and NH documents were reviewed after each iteration of the NLP algorithm to determine the false positive rate of the extracted concepts. Results The corpus consisted of 48% H and 52% NH documents as determined by human review. The NLP algorithm successfully extracted concepts from these documents. The H set had an average of 8 concepts related to homelessness per document (median 8, range 1 to 34). The NH set had an average 2 concepts (median 1, range 1 to 13)”. Thirteen template patterns were identified in this set of documents. The three most common were check boxes with square brackets, Yes/No and free text answer after a question. Several positively and negatively asserted concepts were noted to be in the responses to templated questions such as “Are you currently homeless: Yes or No”; “How many times have you been homeless in the past 3 years: (free text response)”; “Have you ever been in jail? [Y] or [N]”; Are you in need of substance abuse services? Yes or No”. Human review of a random sample of documents at the concept level indicated that the NLP algorithm generated 28% false positives in extracting concepts related to homelessness when templates were ignored among the H documents. When the algorithm was refined to include templates, the false positive rate declined to 22%. For the NH documents, the corresponding false positive rates were 56% and 21%. Conclusions To our knowledge, this is one of the first attempts to address the problem of information extraction from templates or templated sections of the EMR. A key challenge of templates is that they will most likely lead to poor performance of NLP algorithms and cause bottlenecks in processing if they are not considered. Acknowledging the presence of templates and refining NLP algorithms to handle them improves information extraction from free text medical notes, thus creating an opportunity for improved surveillance using the EMR. Algorithms will likely need to be customized to the electronic medical record and the surveillance domain of interest. A more detailed analysis of the templated sections is underway.