Insights on the Future of Digital Health: An Analysis of Twitter Posts of IMIA Fellows.

The COVID-19 pandemic has reshaped technology-enhanced services in health and care organizations globally. As the world pivots towards a post-COVID-19 environment, it is essential to examine emerging trends amongst thought leaders in the health information technology sector. This study queried Twitter feeds of IMIA Fellows from 2013 through 2022, utilizing combinations of sentiment analysis, latent dirichlet allocation, and document analysis methods. The results provided a glimpse of positive sentiment year upon year, with the most negative sentiment prevalent in 2020, due to the onset of the pandemic. The findings from this study can be strategically used to analyze emerging trends in digital health, as well as to shape health IT thought leadership in the post-pandemic landscape.

Aligning the American Health Information Management Association Entry-level Curricula Competencies and Career Map With Industry Job Postings: Cross-sectional Study.

BACKGROUND: The field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA's HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA's career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands. OBJECTIVE: The primary aim of this study is to analyze HIM job postings on a US national job recruiting website to determine whether the job postings align with recognized HIM domains, while the secondary aim is to evaluate the AHIMA career map to determine whether it aligns with the job postings. METHODS: A national job recruitment website was mined electronically (web scraping) using the search term "health information management." This cross-sectional inquiry evaluated job advertisements during a 2-week period in 2021. After the exclusion criteria, 691 job postings were analyzed. Data were evaluated with descriptive statistics and natural language processing (NLP). Soft cosine measures (SCM) were used to determine correlations between job postings and the AHIMA career map, curricular competencies, and curricular considerations. ANOVA was used to determine statistical significance. RESULTS: Of all the job postings, 29% (140/691) were in the Southeast, followed by the Midwest (140/691, 20%), West (131/691,19%), Northeast (94/691, 14%), and Southwest (73/691, 11%). The educational levels requested were evenly distributed between high school diploma (219/691, 31.7%), associate degree (269/691, 38.6%), or bachelor's degree (225/691, 32.5%). A master's degree was requested in only 8% (52/691) of the postings, with 72% (42/58) preferring one and 28% (16/58) requiring one. A Registered Health Information Technologist (RHIT) credential was the most commonly requested (207/691, 29.9%) in job postings, followed by Registered Health Information Administrator (RHIA; 180/691, 26%) credential. SCM scores were significantly higher in the informatics category compared to the coding and revenue cycle (P=.006) and data analytics categories (P<.001) but not significantly different from the information governance category (P=.85). The coding and revenue cycle category had a significantly higher SCM score compared to the data analytics category (P<.001). Additionally, the information governance category was significantly higher than the data analytics category (P<.001). SCM scores were significantly different between each competency category, except there were no differences in the average SCM score between the information protection and revenue cycle management categories (P=.96) and the information protection and data structure, content, and information governance categories (P=.31). CONCLUSIONS: Industry job postings primarily sought degrees, with a master's degree a distant fourth. NLP analysis of job postings suggested that the correlation between the informatics category and job postings was higher than that of the coding, revenue cycle, and data analytics categories.

Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management.

OBJECTIVE: This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professionals. METHODS: A literature review was conducted of both peer-reviewed literature and published opinions on current and future use of AI technology to collect, store, and use healthcare data. The authors also sought insights from key HIM leaders via semi-structured interviews conducted both on the phone and by email. RESULTS: The following HIM practices are impacted by AI technologies: 1) Automated medical coding and capturing AI-based information; 2) Healthcare data management and data governance; 3) Fbtient privacy and confidentiality; and 4) HIM workforce training and education. DISCUSSION: HIM professionals must focus on improving the quality of coded data that is being used to develop AI applications. HIM professional's ability to identify data patterns will be an important skill as automation advances, though additional skills in data analysis tools and techniques are needed. In addition, HIM professionals should consider how current patient privacy practices apply to AI application, development, and use. CONCLUSIONS: AI technology will continue to evolve as will the role of HIM professionals who are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge for HIM professionals is to identify leading practices for the management of healthcare data and information in an AI-enabled world.

Assessing Metadata Quality of a Federally Sponsored Health Data Repository.

The U.S. Federal Government developed HealthData.gov to disseminate healthcare datasets to the public. Metadata is provided for each datasets and is the sole source of information to find and retrieve data. This study employed automated quality assessments of the HealthData.gov metadata published from 2012 to 2014 to measure completeness, accuracy, and consistency of applying standards. The results demonstrated that metadata published in earlier years had lower completeness, accuracy, and consistency. Also, metadata that underwent modifications following their original creation were of higher quality. HealthData.gov did not uniformly apply Dublin Core Metadata Initiative to the metadata, which is a widely accepted metadata standard. These findings suggested that the HealthData.gov metadata suffered from quality issues, particularly related to information that wasn't frequently updated. The results supported the need for policies to standardize metadata and contributed to the development of automated measures of metadata quality.

Indexing Publicly Available Health Data with Medical Subject Headings (MeSH): An Evaluation of Term Coverage.

As part of the Open Government Initiative, the United States federal government published datasets to increase collaboration, transparency, consumer participation, and research, and are available online at HealthData.gov. Currently, HealthData.gov does not adequately support the accessibility goal of the Open Government Initiative due to issues of retrieving relevant data because of inadequately cataloguing and lack of indexing with a standardized terminology. Given the commonalities between the HealthData.gov and MEDLINE metadata, Medical Subject Headings (MeSH) may offer an indexing solution, but there needs to be a formal evaluation of the efficacy of MeSH for covering the dataset concepts. The purpose of this study was to determine if MeSH adequately covers the HealthData.gov concepts. The noun and noun phrases from the HealthData.gov metadata were extracted and mapped to MeSH using MetaMap. The frequency of no exact, partical and no matches with MeSH terms were determined. The results of this study revealed that about 70% of the HealthData.gov concepts partially or exactly matched MeSH terms. Therefore, MeSH may be a favorable terminology for indexing the HealthData.gov datasets.

Meaningful Use Attestations among US Hospitals: The Growing Rural-Urban Divide.

The purpose of this study was to assess EHR Incentive Program attestations of eligible US hospitals across geography and hospital type. The proportions of attestations were compared between metropolitan, micropolitan, and rural hospitals and by whether a hospital was critical access or prospective payment system. From 2011 until December 2013, rural and critical access hospitals were attesting to meaningful use and receiving federal incentive payments at a significantly lower proportion than their urban counterparts. The data suggest that the digital divide between urban and rural hospitals that are adopting electronic health records and using the technology effectively is widening. These findings illustrate that the needs of rural hospitals currently and into the future are different than urban hospitals, and the meaningful use program does not appear to provide the resources needed to propel these rural hospitals forward.

Medical Subject Headings (MeSH) for indexing and retrieving open-source healthcare data.

The US federal government initiated the Open Government Directive where federal agencies are required to publish high value datasets so that they are available to the public. Data.gov and the community site Healthdata.gov were initiated to disperse such datasets. However, data searches and retrieval for these sites are keyword driven and severely limited in performance. The purpose of this paper is to address the issue of extracting relevant open-source data by proposing a method of adopting the MeSH framework for indexing and data retrieval. A pilot study was conducted to compare the performance of traditional keywords to MeSH terms for retrieving relevant open-source datasets related to "mortality". The MeSH framework resulted in greater sensitivity with comparable specificity to the keyword search. MeSH showed promise as a method for indexing and retrieving data, yet future research should conduct a larger scale evaluation of the performance of the MeSH framework for retrieving relevant open-source healthcare datasets.

Why do physicians have difficulty accepting clinical decision support systems?

Clinical decision support systems (CDSS) have not consistently demonstrated improvements in clinical care. This may partly be due to the lack of user acceptance. The purpose of this paper was to conduct a systematic literature review and task analysis to develop a model for CDSS design in order to achieve user acceptance.

Validation of an ELISA for urinary dopamine: applications in monitoring treatment of dopamine-related disorders.

Dopamine is a catecholamine that serves as a neurotransmitter in the central and peripheral nervous system. Non-invasive, reliable, and high-throughput techniques for its quantification are needed to assess dysfunctions of the dopaminergic system and monitor therapies. We developed and validated a competitive ELISA for direct determination of dopamine in urine samples. The method provides high specificity, good accuracy, and precision (average inter-assay variation < 12%). The analysis is not affected by general urinary components and structurally related drugs and metabolites. The correlation between ELISA and LC-MS/MS analyses was very good (r = 0.986, n = 28). The reference range was 64-261 µg/g Cr (n = 64). Week-to-week biological variations of second morning urinary dopamine under free-living conditions were 23.9% for within- and 35.5% for between-subject variation (n = 10). The assay is applied in monitoring Parkinson's disease patients under different treatments. Urinary dopamine levels significantly increase in a dose-dependent manner for Parkinson's disease patients under l-DOPA treatment. The present ELISA provides a cost-effective alternative to chromatographic methods to monitor patients receiving dopamine restoring treatment to ensure appropriate dosing and clinical efficacy. The method can be used in pathological research for the assessment of possible peripheral biological markers for disorders related to the dopaminergic system.

Evaluation of a novel ELISA for serotonin: urinary serotonin as a potential biomarker for depression.

Depression is a common disorder with physical and psychological manifestations often associated with low serotonin. Since noninvasive diagnostic tools for depression are sparse, we evaluated the clinical utility of a novel ELISA for the measurement of serotonin in urine from depressed subjects and from subjects under antidepressant therapy. We developed a competitive ELISA for direct measurement of serotonin in derivatized urine samples. Assay performance was evaluated and applied to clinical samples. The analytical range of the assay was from 6.7 to 425 µg serotonin/g creatinine (Cr). The limit of quantification was 4.7 µg/g Cr. The average recovery for spiked urine samples was 104.4%. Average intra-assay variation was 4.4%, and inter-assay variation was <20%. The serotonin analysis was very specific. No significant interferences were observed for 44 structurally and nonstructurally related urinary substances. Very good correlation was observed between urinary serotonin levels measured by ELISA and liquid chromatography tandem mass spectrometry (LC-MS/MS; ELISA = 1.16 × LC-MS/MS - 53.8; r = 0.965; mean % bias = 11%; n = 18). Serotonin was stable in acidified urine for 30 days at room temperature and at -20 °C. The established reference range for serotonin was 54-366 µg/g Cr (n = 64). Serotonin levels detected in depressed patients (87.53 ± 4.89 µg/g Cr; n = 60) were significantly lower (p < 0.001) than in nondepressed subjects (153.38 ± 7.99 µg/g Cr). Urinary excretion of serotonin in depressed individuals significantly increased after antidepressant treatment by 5-hydroxy-tryptophane and/or selective serotonin re-uptake inhibitor (p < 0.01). The present ELISA provides a convenient and robust method for monitoring urinary serotonin. It is suitable to monitor serotonin imbalances and may be particularly helpful in evaluating antidepressant therapies.

Neurotransmitters excreted in the urine as biomarkers of nervous system activity: validity and clinical applicability.

Strategies for managing the nervous system are numerous while methods of evaluating the nervous system are limited. Given the physiological importance of neurotransmitters as signaling molecules in the nervous system, the measurement of neurotransmitters has significant potential as a clinical tool. Of all the biological fluids that can be utilized, urinary neurotransmitter testing, due to its stability, sensitivity, and non-invasiveness, is the desired method to analyze nervous system function. Increasing use of this technology in a clinical setting demands a review of its feasibility, utility, and clinical value. We review the current body of literature pertaining to the mechanism of neurotransmitter transport across the blood-brain barrier as well as neurotransmitter filtration and excretion by the kidneys. In addition, this review summarizes the historical use of urinary neurotransmitter assessment to diagnose pheochromocytoma. Early research also correlated urinary assessment of neurotransmitters to various clinical symptoms and treatments of which we present research only for depression, ADHD, and inflammation because of the abundant amount of research in these areas. Finally, we review the limitations and challenges of urinary neurotransmitter testing. Taken together, evidence suggests that neurotransmitters excreted in the urine may have a place in clinical practice as a biomarker of nervous system function to effectively assess disturbances and monitor treatment efficacy.