Building Best Practices for Telehealth Record Documentation in the COVID-19 Pandemic.

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.

Patient clinical documentation in telehealth environment: are we collecting appropriate and sufficient information for best practice?

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.

Population Health: Identifying Skill Sets and Education Alignment for HIM Professionals.

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.

Homelessness following disability-related discharges from active duty military service in Afghanistan and Iraq.

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.

Identifying Homelessness among Veterans Using VA Administrative Data: Opportunities to Expand Detection Criteria.

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.

Chronic multisymptom illness among female Veterans deployed to Iraq and Afghanistan.

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<0.01). There were statistically significant differences in age, education, marital status, military component, service branch, and proportions of those with depression and/or post-traumatic stress disorder diagnoses across females with and without CMI-related diagnoses. Diagnoses were mainly from primary care, women's health, and physical medicine and rehabilitation clinics. CONCLUSIONS: CMI-related diagnoses were more prevalent among female OEF/OIF/OND Veterans compared with all female Veterans who currently access VHA. Future studies of the role of mental health diagnoses as confounders or mediators of the association of OEF/OIF/OND deployment and CMI are warranted. These and other factors associated with CMI may provide a basis for enhanced screening to facilitate recognition of these conditions. Further work should evaluate models of care and healthcare utilization related to CMI in female Veterans.

Recognizing Questions and Answers in EMR Templates Using Natural Language Processing.

Templated boilerplate structures pose challenges to natural language processing (NLP) tools used for information extraction (IE). Routine error analyses while performing an IE task using Veterans Affairs (VA) medical records identified templates as an important cause of false positives. The baseline NLP pipeline (V3NLP) was adapted to recognize negation, questions and answers (QA) in various template types by adding a negation and slot:value identification annotator. The system was trained using a corpus of 975 documents developed as a reference standard for extracting psychosocial concepts. Iterative processing using the baseline tool and baseline+negation+QA revealed loss of numbers of concepts with a modest increase in true positives in several concept categories. Similar improvement was noted when the adapted V3NLP was used to process a random sample of 318,000 notes. We demonstrate the feasibility of adapting an NLP pipeline to recognize templates.

Detecting earlier indicators of homelessness in the free text of medical records.

Early warning indicators to identify US Veterans at risk of homelessness are currently only inferred from administrative data. References to indicators of risk or instances of homelessness in the free text of medical notes written by Department of Veterans Affairs (VA) providers may precede formal identification of Veterans as being homeless. This represents a potentially untapped resource for early identification. Using natural language processing (NLP), we investigated the idea that concepts related to homelessness written in the free text of the medical record precede the identification of homelessness by administrative data. We found that homeless Veterans were much higher utilizers of VA resources producing approximately 12 times as many documents as non-homeless Veterans. NLP detected mentions of either direct or indirect evidence of homelessness in a significant portion of Veterans earlier than structured data.

Exploring patterns in resource utilization prior to the formal identification of homelessness in recently returned veterans.

There are limited data on resources utilized by US Veterans prior to their identification as being homeless. We performed visual analytics on longitudinal medical encounter data prior to the official recognition of homelessness in a large cohort of OEF/OIF Veterans. A statistically significant increase in numbers of several categories of visits in the immediate 30 days prior to the recognition of homelessness was noted as compared to an earlier period. This finding has the potential to inform prediction algorithms based on structured data with a view to intervention and mitigation of homelessness among Veterans.

Extracting Concepts Related to Homelessness from the Free Text of VA Electronic Medical Records.

Mining the free text of electronic medical records (EMR) using natural language processing (NLP) is an effective method of extracting information not always captured in administrative data. We sought to determine if concepts related to homelessness, a non-medical condition, were amenable to extraction from the EMR of Veterans Affairs (VA) medical records. As there were no off-the-shelf products, a lexicon of terms related to homelessness was created. A corpus of free text documents from outpatient encounters was reviewed to create the reference standard for NLP training and testing. V3NLP Framework was used to detect instances of lexical terms and was compared to the reference standard. With a positive predictive value of 77% for extracting relevant concepts, this study demonstrates the feasibility of extracting positively asserted concepts related to homelessness from the free text of medical records.

"Sitting on pins and needles": characterization of symptom descriptions in clinical notes".

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.

Validating a strategy for psychosocial phenotyping using a large corpus of clinical text.

OBJECTIVE: To develop algorithms to improve efficiency of patient phenotyping using natural language processing (NLP) on text data. Of a large number of note titles available in our database, we sought to determine those with highest yield and precision for psychosocial concepts. MATERIALS AND METHODS: From a database of over 1 billion documents from US Department of Veterans Affairs medical facilities, a random sample of 1500 documents from each of 218 enterprise note titles were chosen. Psychosocial concepts were extracted using a UIMA-AS-based NLP pipeline (v3NLP), using a lexicon of relevant concepts with negation and template format annotators. Human reviewers evaluated a subset of documents for false positives and sensitivity. High-yield documents were identified by hit rate and precision. Reasons for false positivity were characterized. RESULTS: A total of 58 707 psychosocial concepts were identified from 316 355 documents for an overall hit rate of 0.2 concepts per document (median 0.1, range 1.6-0). Of 6031 concepts reviewed from a high-yield set of note titles, the overall precision for all concept categories was 80%, with variability among note titles and concept categories. Reasons for false positivity included templating, negation, context, and alternate meaning of words. The sensitivity of the NLP system was noted to be 49% (95% CI 43% to 55%). CONCLUSIONS: Phenotyping using NLP need not involve the entire document corpus. Our methods offer a generalizable strategy for scaling NLP pipelines to large free text corpora with complex linguistic annotations in attempts to identify patients of a certain phenotype.

Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans.

Information retrieval algorithms based on natural language processing (NLP) of the free text of medical records have been used to find documents of interest from databases. Homelessness is a high priority non-medical diagnosis that is noted in electronic medical records of Veterans in Veterans Affairs (VA) facilities. Using a human-reviewed reference standard corpus of clinical documents of Veterans with evidence of homelessness and those without, an open-source NLP tool (Automated Retrieval Console v2.0, ARC) was trained to classify documents. The best performing model based on document level work-flow performed well on a test set (Precision 94%, Recall 97%, F-Measure 96). Processing of a naïve set of 10,000 randomly selected documents from the VA using this best performing model yielded 463 documents flagged as positive, indicating a 4.7% prevalence of homelessness. Human review noted a precision of 70% for these flags resulting in an adjusted prevalence of homelessness of 3.3% which matches current VA estimates. Further refinements are underway to improve the performance. We demonstrate an effective and rapid lifecycle of using an off-the-shelf NLP tool for screening targets of interest from medical records.

Prevalence of esophageal atresia among 18 international birth defects surveillance programs.

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], 2.35-2.53) 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 3954-4251 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.