AI-Based Healthcare Analytics for Predictive Modeling and Data Integration

Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders to derive meaningful latent spaces from data with spatially global similarities and local differences, such as electronic health records (EHR) and medical imaging. We investigate batch size effects in both EHR data from the Baltimore Longitudinal Study of Aging and medical imaging data from the multimodal brain tumor segmentation (BraTS) challenge. We train fully connected and convolutional autoencoders to compress the EHR and imaging input spaces, respectively, into 32-dimensional latent spaces via reconstruction losses for various batch sizes between 1 and 100. Under the same hyperparameter configurations, smaller batches improve loss performance for both datasets. Additionally, latent spaces derived by autoencoders with smaller batches capture more biologically meaningful information. Qualitatively, we visualize 2-dimensional projections of the latent spaces and find that with smaller batches the EHR network better separates the sex of the individuals, and the imaging network better captures the right-left laterality of tumors. Quantitatively, the analogous sex classification and laterality regressions using the latent spaces demonstrate statistically significant improvements in performance at smaller batch sizes. Finally, we find improved individual variation locally in visualizations of representative data reconstructions at lower batch sizes. Taken together, these results suggest that smaller batch sizes should be considered when designing autoencoders to extract meaningful latent spaces among EHR and medical imaging data driven by global similarities and local variation.

To examine the value of unstructured electronic health record (EHR) data (free-text notes) in identifying a set of geriatric syndromes. Retrospective analysis of unstructured EHR notes using a natural language processing (NLP) algorithm. Large multispecialty group. Older adults (N=18,341; average age 75.9, 58.9% female). We compared the number of geriatric syndrome cases identified using structured claims and structured and unstructured EHR data. We also calculated these rates using a population-level claims database as a reference and identified comparable epidemiological rates in peer-reviewed literature as a benchmark. Using insurance claims data resulted in a geriatric syndrome prevalence ranging from 0.03% for lack of social support to 8.3% for walking difficulty. Using structured EHR data resulted in similar prevalence rates, ranging from 0.03% for malnutrition to 7.85% for walking difficulty. Incorporating unstructured EHR notes, enabled by applying the NLP algorithm, identified considerably higher rates of geriatric syndromes: absence of fecal control (2.1%, 2.3 times as much as structured claims and EHR data combined), decubitus ulcer (1.4%, 1.7 times as much), dementia (6.7%, 1.5 times as much), falls (23.6%, 3.2 times as much), malnutrition (2.5%, 18.0 times as much), lack of social support (29.8%, 455.9 times as much), urinary retention (4.2%, 3.9 times as much), vision impairment (6.2%, 7.4 times as much), weight loss (19.2%, 2.9 as much), and walking difficulty (36.34%, 3.4 as much). The geriatric syndrome rates extracted from structured data were substantially lower than published epidemiological rates, although adding the NLP results considerably closed this gap. Claims and structured EHR data give an incomplete picture of burden related to geriatric syndromes. Geriatric syndromes are likely to be missed if unstructured data are not analyzed. Pragmatic NLP algorithms can assist with identifying individuals at high risk of experiencing geriatric syndromes and improving coordination of care for older adults.

The volume of data in the world is growing very fast and generated from verity of sources like social media, sensors airline industry or scientific data in different formats. Biggest challenge is how to infer meaningful insights from such a varietyful and big data along with concern of data storage and management of fast growing data. The size of the databases used in today’s enterprises has been growing at exponential rates day by day. Hence, industries requirement to quickly process and analyze the big data volumes for business decision making and customer insights has also grown exponentially. Data pouring from various sources may be can be structured or unstructured in nature. Structured data refers to a relatively well-organized information, which can be further inserted into traditional RDBMS. As Traditional RDBMS are efficient and easy queries by simple, straightforward search algorithms or SQL queries. In contrast to structured data, unstructured data can be considered as information, which does not, comes in a pre-defined data format, well organized data storage model, or cannot be stored well into relational tables. It is assumed to be fastest growing type of data, e.g. image, sensors data, web chats, social networking messaging data, video, documents, log files, and email data. There are many techniques and software available, which can process and provide efficient storage of unstructured data and help organization to perform analytics on unstructured data. Unstructured data does not well-organized and not stored in predefined manner e.g. logs, web chats. The variety and on ordered nature of data makes storage methods and structure makes execution a time and resource-consuming affair. Advancement into technology has open floodgates to push huge volume of unstructured type of data. Multimedia data is one of the example of unstructured big data, which spans all over the Internet. This needs high execution capability to extract useful information. Rapid processing of multimedia data such as video is important for e.g. criminal investigations, surveillance monitoring, news analysis, sports analytics domain, emotion extraction, etc. Hence, analysis of multimedia data in minimum timeframe is one of the latest research areas. Therefore, we have researched techniques for analyzing unstructured data to extract meaningful information hidden in the big data. In addition, we will describe about various techniques and software used to Manage, process unstructured big data in efficient manner, and increases the performance of complexity analysis.

Objective: To evaluate the continuity and completeness of electronic health record (EHR) data, and the concordance of select clinical outcomes and baseline comorbidities between EHR and linked claims data, from three healthcare delivery systems in Taiwan. Methods: We identified oral hypoglycemic agent (OHA) users from the Integrated Medical Database of National Taiwan University Hospital (NTUH-iMD), which was linked to the National Health Insurance Research Database (NHIRD), from June 2011 to December 2016. A secondary evaluation involved two additional EHR databases. We created consecutive 90-day periods before and after the first recorded OHA prescription and defined patients as having continuous EHR data if there was at least one encounter or prescription in a 90-day interval. EHR data completeness was measured by dividing the number of encounters in the NTUH-iMD by the number of encounters in the NHIRD. We assessed the concordance between EHR and claims data on three clinical outcomes (cardiovascular events, nephropathy-related events, and heart failure admission). We used individual comorbidities that comprised the Charlson comorbidity index to examine the concordance of select baseline comorbidities between EHRs and claims. Results: We identified 39,268 OHA users in the NTUH-iMD. Thirty-one percent (n = 12,296) of these users contributed to the analysis that examined data continuity during the 6-month baseline and 24-month follow-up period; 31% (n = 3,845) of the 12,296 users had continuous data during this 30-month period and EHR data completeness was 52%. The concordance of major cardiovascular events, nephropathy-related events, and heart failure admission was moderate, with the NTU-iMD capturing 49–55% of the outcome events recorded in the NHIRD. The concordance of comorbidities was considerably different between the NTUH-iMD and NHIRD, with an absolute standardized difference >0.1 for most comorbidities examined. Across the three EHR databases studied, 29–55% of the OHA users had continuous records during the 6-month baseline and 24-month follow-up period. Conclusion: EHR data continuity and data completeness may be suboptimal. A thorough evaluation of data continuity and completeness is recommended before conducting clinical and translational research using EHR data in Taiwan.

IntroductionThe ability to utilize the electronic health record (EHR) without compromising the doctor-patient relationship (DPR) is an essential skill of all physicians and trainees, yet little time is spent on educating or assessing learners on needed techniques. To address this gap, we developed a conventional OSCE station coupled with a simulated patient chart within the Epic program in order to assess our students' skills utilizing the EHR during a patient encounter.MethodsOf third-year medical students, 119 were given full access to the patient's simulated chart 24 hours in advance of their OSCE to review clinical data. During an in-person OSCE with a standardized patient (SP), students performed a focused history and physical, using the EHR to verify allergies and medications. Students completed an electronic patient note graded by faculty. SPs evaluated the students on communication and interpersonal skills with specific rubric elements. Faculty graded the students' notes to evaluate their expression of clinical reasoning in the assessment and plan.ResultsTraining SPs and faculty to assess students on EHR skills was feasible. After implementation of a comprehensive curriculum focused on EHR and DPR, there was a significant difference on EHR-related communication skills (M = 76.4, SD = 17.6) versus (M = 37, SD = 28.9) before curriculum enhancement t (117.9) = −12.4, p <.001.DiscussionThe EHR OSCE station provided a standardized method of assessing students' EHR skills during a patient encounter. Challenges still exist in the technological requirements to develop and deliver cases in today's EHR platform.

Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique vulnerabilities and etiologies. However, existing approaches fail to fully utilize regional heterogeneous electronic health record (EHR) data. Integrating this data can provide a more comprehensive understanding of depression and anxiety in T2DM patients, leading to more personalized treatment strategies. This study aims to develop and validate a deep learning model, the Regional EHR for Depression and Anxiety Prediction Model (REDAPM), using regional EHR data to predict depression and anxiety in patients with T2DM. A case-control development and validation study was conducted using regional EHR data from the Nanjing Health Information Center (NHIC). Two retrospective, matched (1:3) datasets were constructed from the full cohort for the model's internal and external validation. These two datasets were selected from the NHIC data of 2020 and 2022, respectively. The REDAPM incorporates both structured and unstructured EHR data, capturing the temporal dependency of clinical events. The performance of REDAPM was compared to a set of baseline models, evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and the area under the precision-recall curve (PR-AUC). Subgroup, ablation, and interpretation analyses were conducted to identify relevant clinical features available from EHRs. The internal and external validation datasets comprised 24,724 and 34,340 patients, respectively. The REDAPM outperformed baseline models in both datasets, achieving ROC-AUC scores of 0.9029±0.008 and 0.7360±0.005, and PR-AUC scores of 0.8124±0.011 and 0.5504±0.009. Ablation and subgroup experiments confirmed the significant contribution of patients' medical history text to the model's performance. Integrated gradient score analysis identified the predictive importance of other mental disorders. The REDAPM effectively leverages the heterogeneous characteristics of regional EHR data, demonstrating strong predictive performance for depression onset in diabetic patients. It also shows potential for discovering significant clinical features, indicating considerable promise for clinical utility.

Electronic health record (EHR) data are widely used in public health research, including in HIV-related studies, but are limited by potential bias due to incomplete and inaccurate information, lack of generalizability, and lack of representativeness. This study explores how workflow processes among HIV health care providers (HCPs), data scientists, and state health department professionals may potentially introduce or minimize bias within EHR data. One focus group with 3 health department professionals working in HIV surveillance and 16 in-depth interviews (ie, 5 people with HIV, 5 HCPs, 5 data scientists, and 1 health department professional providing retention-in-care services) were conducted with participants purposively sampled in South Carolina from August 2023 to April 2024. All interviews were transcribed verbatim and analyzed using a constructivist grounded theory approach, where transcripts were first coded and then focused, axial, and theoretically coded. The EHR data lifecycle originates with people with HIV and HCPs in the clinical setting. Data scientists then curate EHR data and health department professionals manage and use the data for surveillance and policy decision-making. Throughout this lifecycle, the three primary stakeholders (ie, HCPs, data scientists, and health department professionals) identified challenges with EHR processes and provided their recommendations and accommodations in addressing the related challenges. HCPs reported the influence of socio-structural biases on their inquiry, interpretation, and documentation of social determinants of health (SDOH) information of people living with HIV, the influence of which is proposed to be mitigated through people living with HIV access to their EHRs. Data scientists identified limited data availability and representativeness as biasing the data they manage. Health department professionals face challenges with delayed and incomplete data, which may be addressed statistically but require consideration of the data's limitations. Overall, bias within the EHR data lifecycle persists because workflows are not intentionally structured to minimize bias and there is a diffusion of responsibility for data quality between the various stakeholders. From the perspective of various stakeholders, this study describes the EHR data lifecycle and its associated challenges as well as stakeholders' accommodations and recommendations for mitigating and eliminating bias in EHR data. Based upon these findings, studies reliant on EHR data should adequately consider its challenges and limitations. Throughout the EHR data lifecycle, bias could be reduced through an inclusive, supportive health care environment, people living with HIV verification of SDOH information, the customization of data collection systems, and EHR data inspection for completeness, accuracy, and timeliness. Future research is needed to further identify instances where bias is introduced and how it can best be mitigated and eliminated across the EHR data lifecycle. Systematic changes are necessary to reduce instances of bias between data workflows and stakeholders.

BackgroundMedical referral is the transfer of a patient’s care from one physician to another upon request. This process involves multiple steps that require provider-to-provider and provider-to-patient communication. In Taiwan, the National Health Insurance Administration (NHIA) has implemented a national medical referral (NMR) system, which encourages physicians to refer their patients to different health care facilities to reduce unnecessary hospital visits and the financial stress on the national health insurance. However, the NHIA’s NMR system is a government-based electronic medical referral service, and its referral data access and exchange are limited to authorized clinical professionals using their national health smart cards over the NHIA virtual private network. Therefore, this system lacks scalability and flexibility and cannot establish trusting relationships among patients, family doctors, and specialists.ObjectiveTo eliminate the existing restrictions of the NHIA’s NMR system, this study developed a scalable, flexible, and blockchain-enabled framework that leverages the NHIA’s NMR referral data to build an alliance-based medical referral service connecting health care facilities.MethodsWe developed a blockchain-enabled framework that can integrate patient referral data from the NHIA’s NMR system with electronic medical record (EMR) and electronic health record (EHR) data of hospitals and community-based clinics to establish an alliance-based medical referral service serving patients, clinics, and hospitals and improve the trust in relationships and transaction security. We also developed a blockchain-enabled personal health record decentralized app (DApp) based on our blockchain-enabled framework for patients to acquire their EMR and EHR data; DApp access logs were collected to assess patients’ behavior and investigate the acceptance of our personal authorization-controlled framework.ResultsThe constructed iWellChain Framework was installed in an affiliated teaching hospital and four collaborative clinics. The framework renders all medical referral processes automatic and paperless and facilitates efficient NHIA reimbursements. In addition, the blockchain-enabled iWellChain DApp was distributed for patients to access and control their EMR and EHR data. Analysis of 3 months (September to December 2018) of access logs revealed that patients were highly interested in acquiring health data, especially those of laboratory test reports.ConclusionsThis study is a pioneer of blockchain applications for medical referral services, and the constructed framework and DApp have been applied practically in clinical settings. The iWellChain Framework has the scalability to deploy a blockchain environment effectively for health care facilities; the iWellChain DApp has potential for use with more patient-centered applications to collaborate with the industry and facilitate its adoption.

The increase in hospitalizations of patients with noncommunicable diseases remains a key challenge for healthcare systems. A significant portion of these hospitalizations is potentially preventable with timely outpatient monitoring and effective primary care. Predictive models based on data from health information systems (HIS) and electronic health records (EHRs) make it possible to identify patients at increased risk of hospitalization and improve their management strategies. The article systematizes and summarizes international experience in using hospitalization risk prognostic models, developed on the basis of HIS and EHR data. We made a systematic review of publications presented in Russian and foreign databases (Scopus, PubMed, BMC Health Services Research, BMJ Research, Google Scholar, Elibrary, Oxford Academic, Wiley Online Library) for the period 1993-2023. The analysis included studies that used EHR and HIS data to develop and/or validate prognostic models of hospitalization risk. The information is systematized by following predictor categories: diseases and comorbidities, prescriptions, interaction with healthcare organizations, sociodemographic and laboratory parameters. The final analysis included 14 studies describing 28 models for predicting the hospitalization risk. Most studies used logistic regression. Machine learning methods (gradient boosting, random forest, and Bayesian ensemble models) were used in some studies. The categories with the highest prognostic value were "diseases and comorbidities" (42,3%), "medication prescriptions" (23,6%), and "interaction with health organizations" (19,8%). Including temporal characteristics (frequency and recency of hospitalizations, visits to primary care facilities, and laboratory tests) increased the model accuracy. Predictive models based on EHR and HIS data demonstrate high discriminatory power and enable the assessment of hospitalization risk at the primary care level. Machine learning methods in some studies demonstrated the possibility of a more in-depth analysis of the relationships between predictors and improved prediction accuracy. A promising area of research is the further development, external validation, and adaptation of models using expanded EHR and HIS data sets for its use in outpatient monitoring and preventing noncommunicable diseases.

BackgroundAmyotrophic lateral sclerosis (ALS) leads to rapid physiological and functional decline before causing untimely death. Current best-practice approaches to interdisciplinary care are unable to provide adequate monitoring of patients’ health. Passive in-home sensor systems enable 24×7 health monitoring. Combining sensor data with outcomes extracted from the electronic health record (EHR) through a supervised machine learning algorithm may enable health care providers to predict and ultimately slow decline among people living with ALS.ObjectiveThis study aims to describe a federated approach to assimilating sensor and EHR data in a machine learning algorithm to predict decline among people living with ALS.MethodsSensor systems have been continuously deployed in the homes of 4 participants for up to 330 days. Sensors include bed, gait, and motion sensors. Sensor data are subjected to a multidimensional streaming clustering algorithm to detect changes in health status. Specific health outcomes are identified in the EHR and extracted via the REDCap (Research Electronic Data Capture; Vanderbilt University) Fast Healthcare Interoperability Resource directly into a secure database.ResultsAs of this writing (fall 2024), machine learning algorithms are currently in development to predict those health outcomes from sensor-detected changes in health status. This methodology paper presents preliminary results from one participant as a proof of concept. The participant experienced several notable changes in activity, fluctuations in heart rate and respiration rate, and reductions in gait speed. Data collection will continue through 2025 with a growing sample.ConclusionsThe system described in this paper enables tracking the health status of people living with ALS at unprecedented levels of granularity. Combined with tightly integrated EHR data, we anticipate building predictive models that can identify opportunities for health care services before adverse events occur. We anticipate that this system will improve and extend the lives of people living with ALS.International Registered Report Identifier (IRRID)DERR1-10.2196/60437

BACKGROUND Amyotrophic lateral sclerosis (ALS) leads to rapid physiological and functional decline before causing untimely death. Current best-practice approaches to interdisciplinary care are unable to provide adequate monitoring of patients’ health. Passive in-home sensor systems enable 24×7 health monitoring. Combining sensor data with outcomes extracted from the electronic health record (EHR) through a supervised machine learning algorithm may enable health care providers to predict and ultimately slow decline among people living with ALS. OBJECTIVE This study aims to describe a federated approach to assimilating sensor and EHR data in a machine learning algorithm to predict decline among people living with ALS. METHODS Sensor systems have been continuously deployed in the homes of 4 participants for up to 330 days. Sensors include bed, gait, and motion sensors. Sensor data are subjected to a multidimensional streaming clustering algorithm to detect changes in health status. Specific health outcomes are identified in the EHR and extracted via the REDCap (Research Electronic Data Capture; Vanderbilt University) Fast Healthcare Interoperability Resource directly into a secure database. RESULTS As of this writing (fall 2024), machine learning algorithms are currently in development to predict those health outcomes from sensor-detected changes in health status. This methodology paper presents preliminary results from one participant as a proof of concept. The participant experienced several notable changes in activity, fluctuations in heart rate and respiration rate, and reductions in gait speed. Data collection will continue through 2025 with a growing sample. CONCLUSIONS The system described in this paper enables tracking the health status of people living with ALS at unprecedented levels of granularity. Combined with tightly integrated EHR data, we anticipate building predictive models that can identify opportunities for health care services before adverse events occur. We anticipate that this system will improve and extend the lives of people living with ALS. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/60437

We assessed the suitability of pooled electronic health record (EHR) data from clinical research networks (CRNs) of the patient-centered outcomes research network to conduct studies of the association between tumor necrosis factor inhibitors (TNFi) and infections. EHR data from patients with one of seven autoimmune diseases were obtained from three CRNs and pooled. Person-level linkage of CRN data and Centers for Medicare and Medicaid Services (CMS) fee-for-service claims data was performed where possible. Using filled prescriptions from CMS claims data as the gold standard, we assessed the misclassification of EHR-based new (incident) user definitions. Among new users of TNFi, we assessed subsequent rates of hospitalized infection in EHR and CMS data. The study included 45 483 new users of TNFi, of whom 1416 were successfully linked to their CMS claims. Overall, 44% of new EHR TNFi prescriptions were not associated with medication claims. Our most specific new user definition had a misclassification rate of 3.5%-16.4% for prevalent use, depending on the medication. Greater than 80% of CRN prescriptions had either zero refills or missing refill data. Compared to using EHR data alone, there was a 2- to 8-fold increase in hospitalized infection rates when CMS claims data were added to the analysis. EHR data substantially misclassified TNFi exposure and underestimated the incidence of hospitalized infections compared to claims data. EHR-based new user definitions were reasonably accurate. Overall, using CRN data for pharmacoepidemiology studies is challenging, especially for biologics, and would benefit from supplementation by other sources.

Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high-risk population. Identifying reasons for statin nonuse, which are typically described in unstructured electronic health record data, can inform targeted system interventions to improve statin use. We aimed to leverage a deep learning approach to identify reasons for statin nonuse in patients with diabetes. Methods and Results Adults with diabetes and no statin prescriptions were identified from a multiethnic, multisite Northern California electronic health record cohort from 2014 to 2020. We used a benchmark deep learning natural language processing approach (Clinical Bidirectional Encoder Representations from Transformers) to identify statin nonuse and reasons for statin nonuse from unstructured electronic health record data. Performance was evaluated against expert clinician review from manual annotation of clinical notes and compared with other natural language processing approaches. Of 33 461 patients with diabetes (mean age 59±15 years, 49% women, 36% White patients, 24% Asian patients, and 15% Hispanic patients), 47% (15 580) had no statin prescriptions. From unstructured data, Clinical Bidirectional Encoder Representations from Transformers accurately identified statin nonuse (area under receiver operating characteristic curve [AUC] 0.99 [0.98-1.0]) and key patient (eg, side effects/contraindications), clinician (eg, guideline-discordant practice), and system reasons (eg, clinical inertia) for statin nonuse (AUC 0.90 [0.86-0.93]) and outperformed other natural language processing approaches. Reasons for nonuse varied by clinical and demographic characteristics, including race and ethnicity. Conclusions A deep learning algorithm identified statin nonuse and actionable reasons for statin nonuse in patients with diabetes. Findings may enable targeted interventions to improve guideline-directed statin use and be scaled to other evidence-based therapies.

Comparison of Lactation Information from Electronic Health Records with Survey Data Across Five US Health Systems.

AB1206 SELF-REPORT OF FRACTURE HISTORY COMPARED TO FRACTURE CODES FROM AN ELECTRONIC HEALTH RECORD DATASET