HITECH Act Drove Large Gains In Hospital Electronic Health Record Adoption

Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions

In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force. Urogynecology, a discipline anchored in intricate patient histories and meticulous follow-ups, is on the brink of profound transformation due to these digital strides. While EHRs have unified patient data, challenges related to data privacy, interoperability, and access persist. In response, we present Pelvic Health Place (PHPlace) - a multilingual, patient-centric application. Purposefully designed to bolster patient engagement, PHPlace provides clinicians with essential pre-consultation insights, streamlines the consent process, vividly delineates surgical pathways, and assures comprehensive long-term monitoring. This platform also establishes a foundation for global data amalgamation, promising to invigorate research and potentially harness artificial intelligence (AI) capabilities. With AI integration, we anticipate a more tailored treatment approach and enriched patient education, signaling a pivotal shift in urogynecology and emphasizing the imperative for ongoing academic inquiry.

Enhancing nursing home quality through electronic health record implementation

Background: The quality of care in nursing homes (NHs) in the United States has long been a matter of policy concern. Although electronic health records (EHRs) are argued to improve quality, implementation has lagged due to various factors such as financial constraints and limited research on their impact on NH quality. Objective: This study examined the relationship between EHR implementation and NH quality using Donabedian's structure-process-outcome model. Method: Data on EHR implementation were collected via a 2018 survey of all Federally certified Arkansas NHs (n = 223). Of the 63 responding NHs, 48 reported EHR implementation. Survey data were merged with secondary sources such as Certification and Survey Provider Enhanced Reporting. A total of 744 NH-years for the period 2008-2020 were included in the final sample. A pre-post negative binomial panel data regression was used to examine the relationship between EHR implementation (dichotomous variable) and NH deficiencies (dependent count variable) with facility/community-level control variables. Results were reported as incidence rate ratios (IRR). Results: NHs that had implemented EHR experienced an 18% reduction in the rate of deficiencies compared to those without EHR systems (IRR = 0.82, 95% CI [0.70, 0.99], p = 0.035). Conclusion: EHR implementation had a favourable impact on NH quality. Implications: Past research suggests that higher NH quality may be associated with improved financial performance. Therefore, EHR implementation has the potential to address two critical challenges: enhancing care quality and improving financial outcomes. However, government financial incentives may be necessary to address the high-cost of implementing EHR systems.

Digital Maturity as a Predictor of Quality and Safety Outcomes in US Hospitals: Cross-Sectional Observational Study

BACKGROUND

This study demonstrates that digital maturity contributes to strengthened quality and safety performance outcomes in US hospitals. Advanced digital maturity is associated with more digitally enabled work environments with automated flow of data across information systems to enable clinicians and leaders to track quality and safety outcomes. This research illustrates that an advanced digitally enabled workforce is associated with strong safety leadership and culture and better patient health and safety outcomes.

OBJECTIVE

This study aimed to examine the relationship between digital maturity and quality and safety outcomes in US hospitals.

METHODS

The data sources were hospital safety letter grades as well as quality and safety scores on a continuous scale published by The Leapfrog Group. We used the digital maturity level (measured using the Electronic Medical Record Assessment Model [EMRAM]) of 1026 US hospitals. This was a cross-sectional, observational study. Logistic, linear, and Tweedie regression analyses were used to explore the relationships among The Leapfrog Group's Hospital Safety Grades, individual Leapfrog safety scores, and digital maturity levels classified as advanced or fully developed digital maturity (EMRAM levels 6 and 7) or underdeveloped maturity (EMRAM level 0). Digital maturity was a predictor while controlling for hospital characteristics including teaching status, urban or rural location, hospital size measured by number of beds, whether the hospital was a referral center, and type of hospital ownership as confounding variables. Hospitals were divided into the following 2 groups to compare safety and quality outcomes: hospitals that were digitally advanced and hospitals with underdeveloped digital maturity. Data from The Leapfrog Group's Hospital Safety Grades report published in spring 2019 were matched to the hospitals with completed EMRAM assessments in 2019. Hospital characteristics such as number of hospital beds were obtained from the CMS database.

RESULTS

The results revealed that the odds of achieving a higher Leapfrog Group Hospital Safety Grade was statistically significantly higher, by 3.25 times, for hospitals with advanced digital maturity (EMRAM maturity of 6 or 7; odds ratio 3.25, 95% CI 2.33-4.55).

CONCLUSIONS

Hospitals with advanced digital maturity had statistically significantly reduced infection rates, reduced adverse events, and improved surgical safety outcomes. The study findings suggest a significant difference in quality and safety outcomes among hospitals with advanced digital maturity compared with hospitals with underdeveloped digital maturity.

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

Call me Dr Ishmael: trends in electronic health record notes available at emergency department visits and admissions

Objectives

Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment.

Materials and Methods

We conducted a retrospective analysis of EHR data from a large healthcare system, examining the number of notes and a corresponding number of total words and total tokens across all notes available to physicians during patient encounters in the emergency department (ED). We assessed the change in these metrics over a 17-year period between 2006 and 2023.

Results

The study cohort included 730 968 ED visits made by 293 559 unique patients and a total note count of 132 574 964. The median note count for all encounters in 2006 was 5 (IQR 1-16), accounting for 1735 (IQR 447-5521) words. By the last full year of the study period, 2022, the median number of notes had grown to 359 (IQR 84-943), representing 359 (IQR 84-943) words. Note and word counts were higher for admitted patients.

Discussion

The volume of notes available for review by providers has increased by over 30-fold in the 17 years since the implementation of the EHR at a large health system. The task of reviewing these notes has become commensurately more difficult. These data point to the critical need for new strategies and tools for filtering, synthesizing, and summarizing information to achieve the promise of the medical record.

Feasibility of a 2-Part Substance Use Screener Self-Administered by Patients on Paper: Observational Study

BACKGROUND

Measurement-based care in behavioral health uses patient-reported outcome measures (PROMs) to screen for mental health symptoms and substance use and to assess symptom change over time. While PROMs are increasingly being integrated into electronic health record systems and administered electronically, paper-based PROMs continue to be used. It is unclear if it is feasible to administer a PROM on paper when the PROM was initially developed for electronic administration.

OBJECTIVE

This study aimed to examine the feasibility of patient self-administration of a 2-part substance use screener-the Tobacco, Alcohol, Prescription medications, and other Substances (TAPS)-on paper. This screener was originally developed for electronic administration. It begins with a limited number of questions and branches to either skip or reflex to additional questions based on an individual's responses. In this study, the TAPS was adapted for paper use due to barriers to electronic administration within an urgent care behavioral health clinic at an urban health safety net hospital.

METHODS

From August 2021 to March 2022, research staff collected deidentified paper TAPS responses and tracked TAPS completion rates and adherence to questionnaire instructions. A retrospective chart review was subsequently conducted to obtain demographic information for the patients who presented to the clinic between August 2021 and March 2022. Since the initial information collected from TAPS responses was deidentified, demographic information was not linked to the individual TAPS screeners that were tracked by research staff.

RESULTS

A total of 507 new patients were seen in the clinic with a mean age of 38.7 (SD 16.6) years. In all, 258 (50.9%) patients were male. They were predominantly Black (n=212, 41.8%), White (n=152, 30%), and non-Hispanic or non-Latino (n=403, 79.5%). Most of the patients were publicly insured (n=411, 81.1%). Among these 507 patients, 313 (61.7%) completed the TAPS screener. Of these 313 patients, 76 (24.3%) adhered to the instructions and 237 (75.7%) did not follow the instructions correctly. Of the 237 respondents who did not follow the instructions correctly, 166 (70%) answered more questions and 71 (30%) answered fewer questions than required in TAPS part 2. Among the 237 patients who did not adhere to questionnaire instructions, 44 (18.6%) responded in a way that contradicted their response in part 1 of the screener and ultimately affected their overall TAPS score.

CONCLUSIONS

It was challenging for patients to adhere to questionnaire instructions when completing a substance use screener on paper that was originally developed for electronic use. When selecting PROMs for measurement-based care, it is important to consider the structure of the questionnaire and how the PROM will be administered to determine if additional support for PROM self-administration needs to be implemented.

An Infrastructure Framework for Remote Patient Monitoring Interventions and Research

Remote patient monitoring (RPM) enables clinicians to maintain and adjust their patients' plan of care by using remotely gathered data, such as vital signs, to proactively make medical decisions about a patient's care. RPM interventions have been touted as a means to improve patient care and well-being while reducing costs and resource needs within the health care ecosystem. However, multiple interworking components must be successfully implemented for an RPM intervention to yield the desired outcomes, and the design and key driver of each component can vary depending on the medical context. This viewpoint and perspective paper presents a 4-component RPM infrastructure framework based on a synthesis of existing literature and practice related to RPM. Specifically, these components are identified and considered: (1) data collection, (2) data transmission and storage, (3) data analysis, and (4) information presentation. Interaction points to consider between components include transmission, interoperability, accessibility, workflow integration, and transparency. Within each of the 4 components, questions affecting research and practice emerge that can affect the outcomes of RPM interventions. This framework provides a holistic perspective of the technologies involved in RPM interventions and how these core elements interact to provide an appropriate infrastructure for deploying RPM in health systems. Further, it provides a common vocabulary to compare and contrast RPM solutions across health contexts and may stimulate new research and intervention opportunities.

Electronic Health Record Implementation Enhances Financial Performance in High Medicaid Nursing Homes

Introduction

The nursing home (NH) industry operates within a two-tiered system, wherein high Medicaid NHs which disproportionately serve marginalized populations, exhibit poorer quality of care and financial performance. Utilizing the resource-based view of the firm, this study aimed to investigate the association between electronic health record (EHR) implementation and financial performance in high Medicaid NHs. A positive correlation could allow high Medicaid NHs to leverage technology to enhance efficiency and financial health, thereby establishing a business case for EHR investments.

Methods

Data from 2017 to 2018 were sourced from mail surveys sent to the Director of Nursing in high Medicaid NHs (defined as having 85% or more Medicaid census, excluding facilities with over 10% private pay or 8% Medicare), and secondary sources like LTCFocus.org and Centers for Medicare & Medicaid Services cost reports. From the initial sample of 1,050 NHs, a 37% response rate was achieved (391 surveys). Propensity score inverse probability weighting was used to account for potential non-response bias. The independent variable, EHR Implementation Score (EIS), was calculated as the sum of scores across five EHR functionalities-administrative, documentation, order entry, results viewing, and clinical tools-and reflected the extent of electronic implementation. The dependent variable, total margin, represented NH financial performance. A multivariable linear regression model was used, adjusting for organizational and market-level control variables that may independently affect NH financial performance.

Results

Approximately 76% of high Medicaid NHs had implemented EHR either fully or partially (n = 391). The multivariable regression model revealed that a one-unit increase in EIS was associated with a 0.12% increase in the total margin (p = 0.05, CI: -0.00-0.25).

Conclusion

The findings highlight a potential business case -long-term financial returns for the initial investments required for EHR implementation. Nonetheless, policy interventions including subsidies may still be necessary to stimulate EHR implementation, particularly in high Medicaid NHs.

Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated Health Care System: Validation Study

Background

Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature.

objectives

We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic.

Methods

Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. κ statistic for interrater reliability among the 2 abstractors was calculated.

Results

The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100%, 100%, 99.2%, and 100%, respectively, for ASD; 100%, 99.9%, 99.2%, and 100%, respectively, for ADHD; 100%, 100%, 100%, and 100%, respectively for DBD; 87.7%, 100%, 100%, and 99.2%, respectively, for AD; and 100%, 100%, 99.2%, and 100%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7% and 100%. The overall agreement between abstractors was almost perfect (κ=95%).

Conclusions

Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system.

The use and perceived value of electronic health information resources by health care professionals in the field of medicine, pharmacy, and nursing in Jazan province, Saudi Arabia

Background

Healthcare workers increasingly use Electronic Health Information Resources (EHIRs) to make evidence-based decisions. Our study was intended to assess the perception, attitude, and practice of healthcare professionals in medicine, pharmacy, and nursing regarding their perceived value and use of EHIRs.

Methods

We conducted an observational cross-sectional study using a pre-validated questionnaire among healthcare professionals in Jazan province from September 2022 to February 2023. We included healthcare professionals and interns with medical, pharmacy, or nursing degrees and excluded those who refused informed consent.

Results

We included fully completed data from 294 participants, with an actual response rate of just 80.1 %. Almost 87.41 % utilized the health information resources at their workplace, with UpToDate [39.45 %] and Medscape [67.01 %] being the most frequently used medical databases. The health facilities' access to electronic health resources significantly impacted healthcare professionals' [p = 0.04] and medical interns' [p = 0.02] roles. Faculty members felt the need to access electronic health information at their workplace [p = 0.00]. Lack of time to access electronic health information due to a busy schedule was a significant reason that impacted the attitude of medical professionals [p = 0.008] and nursing staff [p = 0.025]. An excessive amount of clinically unrelated data was the primary obstacle (181/294, p < 0.0001) in using electronic health information resources.

Conclusion

Our study showed the pattern of healthcare professionals using EHIRs in the Jazan province, Saudi Arabia. We believe the study's outcome can help increase the calibre of electronic health information services available to healthcare professionals and raise awareness of different EHIRs in improving clinical care.

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models

OBJECTIVES

Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data.

MATERIALS AND METHODS

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment.

RESULTS

Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting.

DISCUSSION

This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.

Healthcare on the brink: navigating the challenges of an aging society in the United States

The US healthcare system is at a crossroads. With an aging population requiring more care and a strained system facing workforce shortages, capacity issues, and fragmentation, innovative solutions and policy reforms are needed. This paper aims to spark dialogue and collaboration among healthcare stakeholders and inspire action to meet the needs of the aging population. Through a comprehensive analysis of the impact of an aging society, this work highlights the urgency of addressing this issue and the importance of restructuring the healthcare system to be more efficient, equitable, and responsive.

Comparing "Meaningful Use" of Health Information Technology in Pennsylvania: Electronic Prescription Rates of Metropolitan and Rural Counties

The Health Information Technology for Economic and Clinical Health Act incentivized the adoption of electronic health records (EHRs). Health systems looked to leverage technology to assist in serving populations in health professional shortage areas. Qualitative research points to EHR usability as a source of health inequities in rural settings, making the challenges of EHR usage a subject of interest. Pennsylvania offers a model for investigating rural health infrastructure with it having the third largest rural population in the United States. This study analyzed the adoption of Electronic Prescribing in the 67 Pennsylvania (PA) counties. Physician adoption and usage data for PA and the United States were compared using a t-test to establish a basis for comparison. PA counties were categorized using the United States Department of Agriculture (USDA)'s Rural-Urban Commuting Areas (RUCAs) system. Surescript use percentages were plotted against the RUCA scores of each PA county to create a polynomial regression model. PA office-based physicians, on average, utilize e-prescription tools at the same rate as the national average with 59% of practices utilizing Surescripts as of 2013. There was no significant correlation between Surescript usage and the rural/urban classification of counties in Pennsylvania (R-squared value of 0.06). Pennsylvania was able to adopt health information technology (HIT) infrastructure at the same rate as the national average. Rural and metropolitan definitions do not correlate to meaningful use of HIT, thus usability of HIT cannot be tied to health outcomes. Future studies looking at specific forms of HIT and their ability to decrease the burden of administrative work for clinicians.

Differences in Care Team Response to Patient Portal Messages by Patient Race and Ethnicity

Importance

The COVID-19 pandemic was associated with substantial growth in patient portal messaging. Higher message volumes have largely persisted, reflecting a new normal. Prior work has documented lower message use by patients who belong to minoritized racial and ethnic groups, but research has not examined differences in care team response to messages. Both have substantial ramifications on resource allocation and care access under a new care paradigm with portal messaging as a central channel for patient-care team communication.

Objective

To examine differences in how care teams respond to patient portal messages sent by patients from different racial and ethnic groups.

Design, Setting, and Participants

In a cross-sectional design in a large safety-net health system, response outcomes from medical advice message threads sent from January 1, 2021, through November 24, 2021, from Asian, Black, Hispanic, and White patients were compared, controlling for patient and message thread characteristics. Asian, Black, Hispanic, and White patients with 1 or more adult primary care visits at Boston Medical Center in calendar year 2020 were included. Data analysis was conducted from June 23, 2022, through December 21, 2023.

Exposure

Patient race and ethnicity.

Main Outcomes and Measures

Rates at which medical advice request messages were responded to by care teams and the types of health care professionals that responded.

Results

A total of 39 043 patients were included in the sample: 2006 were Asian, 21 600 were Black, 7185 were Hispanic, and 8252 were White. A total of 22 744 (58.3%) patients were women and mean (SD) age was 50.4 (16.7) years. In 2021, these patients initiated 57 704 medical advice request message threads. When patients who belong to minoritized racial and ethnic groups sent these messages, the likelihood of receiving any care team response was similar, but the types of health care professionals that responded differed. Black patients were 3.95 percentage points (pp) less likely (95% CI, -5.34 to -2.57 pp; P < .001) to receive a response from an attending physician, and 3.01 pp more likely (95% CI, 1.76-4.27 pp; P < .001) to receive a response from a registered nurse, corresponding to a 17.4% lower attending response rate. Similar, but smaller, differences were observed for Asian and Hispanic patients.

Conclusions and Relevance

The findings of this study suggest lower prioritization of patients who belong to minoritized racial and ethnic groups during triaging. Understanding and addressing these disparities will be important for improving care equity and informing health care delivery support algorithms.

Assessing Alternative Approaches for Wound Closure in a National Pediatric Learning Health System

INTRODUCTION

Our objective was to perform a feasibility study using real-world data from a learning health system (LHS) to describe current practice patterns of wound closure and explore differences in outcomes associated with the use of tissue adhesives and other methods of wound closure in the pediatric surgical population to inform a potentially large study.

METHODS

A multi-institutional cross-sectional study was performed of a random sample of patients <18 y-old who underwent laparoscopic appendectomy, open or laparoscopic inguinal hernia repair, umbilical hernia repair, or repair of traumatic laceration from January 1, 2019, to December 31, 2019. Sociodemographic and operative characteristics were obtained from 6 PEDSnet (a national pediatric LHS) children's hospitals and OneFlorida Clinical Research Consortium (a PCORnet collaboration across 14 academic health systems). Additional clinical data elements were collected via chart review.

RESULTS

Of the 692 patients included, 182 (26.3%) had appendectomies, 155 (22.4%) inguinal hernia repairs, 163 (23.6%) umbilical hernia repairs, and 192 (27.8%) traumatic lacerations. Of the 500 surgical incisions, sutures with tissue adhesives were the most frequently used (n = 211, 42.2%), followed by sutures with adhesive strips (n = 176, 35.2%), and sutures only (n = 72, 14.4%). Most traumatic lacerations were repaired with sutures only (n = 127, 64.5%). The overall wound-related complication rate was 3.0% and resumption of normal activities was recommended at a median of 14 d (interquartile ranges 14-14).

CONCLUSIONS

The LHS represents an efficient tool to identify cohorts of pediatric surgical patients to perform comparative effectiveness research using real-world data to support medical and surgical products/devices in children.

Private Payers and Cancer Care: Revisiting the Land of Opportunity

Ten years ago we charted a course for oncology payment reform. We summarize what went wrong and propose ways to fix it.

Public Health Informatics and the Perioperative Physician: Looking to the Future

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Data-Centric Machine Learning in Nursing: A Concept Clarification

The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelligence and machine learning) to assist clinicians and healthcare administrators with decision-making, predictive analytics, and more. Multiple studies have cited various applications for artificial intelligence and machine learning in nursing. However, what is unknown in the nursing discipline is that while greater than 90% of machine-learning implementations use a model-centric strategy, a fundamental change is occurring. Because of the limitations of this approach, the industry is beginning to pivot toward data-centric artificial intelligence. Nurses should be aware of the differences, including how each approach affects their engagement in designing human-intelligent-like technologies and their data usage, especially regarding electronic health records. Using the Norris Concept Clarification method, this article elucidates the data-centric machine learning concept for nursing. This is accomplished by (1) exploring the concept's origins in the data and computer science disciplines; (2) differentiating data- versus model-centric machine learning approaches, including introducing the machine-learning operation life cycle and process; and (3) explaining the advantages of the data-centric phenomenon, especially concerning nurses' engagement in technological design and proper data usage.

Challenges and dynamics of public health reporting and data exchange during COVID-19: insights from US hospitals

The US health care response during the early stages of the COVID-19 pandemic unveiled challenges in public health reporting systems and electronic clinical data exchange. Using data from the 2020 and 2022 American Hospital Association information technology supplement surveys, this study examined US hospitals' experiences in public health reporting, accessing clinical data from external providers for COVID-19 patient care, and their success in reporting vaccine-related adverse events to relevant state and federal agencies. Results showcase significant disparities in reporting practices across government levels due to inconsistent requirements. Although many hospitals leaned toward automated data transmission, a substantial portion continued to depend on manual processes. Pertaining to electronic clinical data, while entities like large commercial laboratories outperformed others, a considerable number were sluggish in delivering critical information. Moreover, a small percentage of hospitals reported challenges in recording vaccine-related adverse events, emphasizing the need for transparent reporting systems. The study underscores the necessity for standardized reporting protocols, explicit directives, and a pivot from manual to automated processes. Tackling these challenges is pivotal for ensuring prompt and reliable data, bolstering future public health responses, and rejuvenating public trust in health institutions.

Adoption of Electronic Health Record Among Substance Use Disorder Treatment Programs: Nationwide Cross-Sectional Survey Study

BACKGROUND

Electronic health record (EHR) systems have been shown to be associated with improvements in care processes, quality of care, and patient outcomes. EHR also has a crucial role in the delivery of substance use disorder (SUD) treatment and is considered important for addressing SUD crises, including the opioid epidemic. However, little is known about the adoption of EHR in SUD treatment programs or the organizational-level factors associated with the adoption of EHR in SUD treatment.

OBJECTIVE

We examined the adoption of EHR in SUD programs, with a focus on changes in adoption from 2014 to 2017, and identified organizational-level factors associated with EHR adoption.

METHODS

We used data from the 2014 and 2017 National Drug Abuse Treatment System Surveys. Our analysis included 1027 SUD programs (531 in 2014 and 496 in 2017). We used chi-square and Mann-Whitney U tests for categorical and continuous variables, respectively, to assess changes in EHR adoption, technology use, program, and client characteristics. We also investigated differences in characteristics and barriers to adoption by EHR adoption status (adopted EHR vs had not adopted or were planning to adopt EHR). We then conducted multivariate logistic regressions to examine internal and external factors associated with EHR adoption.

RESULTS

The adoption of EHR increased significantly from 57.6% (306/531) in 2014 to 69.2% (343/496) in 2017 (P<.001), showing that nearly one-third (153/496, 30.8%) of SUD programs had not yet adopted an EHR system by 2017. We identified a significant increase in technology use and ownership by a parent company (P=.01 and P<.001) and a decrease in the percentage of uninsured patients in 2017 (P<.001), compared to 2014. Our analysis further showed significant differences by adoption status for three major barriers to adoption: (1) start-up costs, (2) ongoing financial costs, and (3) privacy or security concerns (P<.001). Programs that used computerized scheduling (adjusted odds ratio [AOR] 3.02, 95% CI 2.23-4.09) and billing systems (AOR 2.29, 95% CI 1.62-3.25) were more likely to adopt EHR. Similarly, ownership type, such as private nonprofit (AOR 1.86, 95% CI 1.31-2.65) and public (AOR 2.14, 95% CI 1.27-3.67), or interest in participating in a patient-centered medical home (AOR 1.93, 95% CI 1.29-2.92), were associated with an increased likelihood to adopt EHR. Overall, SUD programs were more likely to adopt an EHR system in 2017 compared to 2014 (AOR 1.44, 95% CI 1.07-1.94).

CONCLUSIONS

Our findings highlighted that SUD programs may be on track to achieve widespread EHR adoption. However, there is a need for focused strategies, resources, and policies explicitly designed to systematically address barriers and tackle obstacles to expanding the adoption of EHR systems. These efforts must be holistic and address factors at multiple organizational levels.

Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR)

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

A Conference (Missingness in Action) to Address Missingness in Data and AI in Health Care: Qualitative Thematic Analysis

BACKGROUND

Missingness in health care data poses significant challenges in the development and implementation of artificial intelligence (AI) and machine learning solutions. Identifying and addressing these challenges is critical to ensuring the continued growth and accuracy of these models as well as their equitable and effective use in health care settings.

OBJECTIVE

This study aims to explore the challenges, opportunities, and potential solutions related to missingness in health care data for AI applications through the conduct of a digital conference and thematic analysis of conference proceedings.

METHODS

A digital conference was held in September 2022, attracting 861 registered participants, with 164 (19%) attending the live event. The conference featured presentations and panel discussions by experts in AI, machine learning, and health care. Transcripts of the event were analyzed using the stepwise framework of Braun and Clark to identify key themes related to missingness in health care data.

RESULTS

Three principal themes-data quality and bias, human input in model development, and trust and privacy-emerged from the analysis. Topics included the accuracy of predictive models, lack of inclusion of underrepresented communities, partnership with physicians and other populations, challenges with sensitive health care data, and fostering trust with patients and the health care community.

CONCLUSIONS

Addressing the challenges of data quality, human input, and trust is vital when devising and using machine learning algorithms in health care. Recommendations include expanding data collection efforts to reduce gaps and biases, involving medical professionals in the development and implementation of AI models, and developing clear ethical guidelines to safeguard patient privacy. Further research and ongoing discussions are needed to ensure these conclusions remain relevant as health care and AI continue to evolve.

Pre-pandemic assessment: a decade of progress in electronic health record adoption among U.S. hospitals

As the COVID-19 pandemic loomed, the adoption of electronic health records (EHRs) in US hospitals became a pivotal concern. This study provides a pre-pandemic assessment, highlighting a decade of progress in EHR adoption from 2009 to 2019, with the last available survey conducted from January to June of 2020. It delves into the current EHR adoption rates, variations across different hospital categories, the influence of major vendors, and the challenges in implementing these systems. The study found that basic EHR adoption surged from 6.6% to 81.2%, while comprehensive systems increased from 3.6% to 63.2%. Despite this growth, the findings point to enduring disparities among hospitals, a concentrated market share by 6 vendors (90%), and significant concerns regarding maintenance costs. These insights provide an invaluable snapshot of the state of EHR adoption at the brink of the pandemic, serving as a benchmark to assess hospitals' readiness to utilize digital infrastructure in health care. The conclusions underscore the necessity for strategic policy interventions to encourage a competitive landscape and guarantee equitable access, ultimately strengthening the health care system's responsiveness to global health crises such as COVID-19.

COVID exacerbated the gender disparity in physician electronic health record inbox burden

The COVID-19 pandemic was associated with significant changes to the delivery of ambulatory care, including a dramatic increase in patient messages to physicians. While asynchronous messaging is a valuable communication modality for patients, a greater volume of patient messages is associated with burnout and decreased well-being for physicians. Given that women physicians experienced greater electronic health record (EHR) burden and received more patient messages pre-pandemic, there is concern that COVID may have exacerbated this disparity. Using EHR audit log data of ambulatory physicians at an academic medical center, we used a difference-in-differences framework to evaluate the impact of the pandemic on patient message volume and compare differences between men and women physicians. We found patient message volume increased post-COVID for all physicians, and women physicians saw an additional increase compared to men. Our results contribute to the growing evidence of different communication expectations for women physicians that contribute to the gender disparity in EHR burden.

Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.

Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review

Objective: To evaluate how and from where social risk data are extracted from EHRs for research purposes, and how observed differences may impact study generalizability. Methods: Systematic scoping review of peer-reviewed literature that used patient-level EHR data to assess 1 ± 6 social risk domains: housing, transportation, food, utilities, safety, social support/isolation. Results: 111/9022 identified articles met inclusion criteria. By domain, social support/isolation was most often included (N = 68/111), predominantly defined by marital/partner status (N = 48/68) and extracted from structured sociodemographic data (N = 45/48). Housing risk was defined primarily by homelessness (N = 39/49). Structured housing data was extracted most from billing codes and screening tools (N = 15/30, 13/30, respectively). Across domains, data were predominantly sourced from structured fields (N = 89/111) versus unstructured free text (N = 32/111). Conclusion: We identified wide variability in how social domains are defined and extracted from EHRs for research. More consistency, particularly in how domains are operationalized, would enable greater insights across studies.

The microbiological landscape and epidemiology of ocular infections in a multi-tier ophthalmology network in India: an electronic medical record driven analytics report

OBJECTIVE

To study the epidemiology and landscape of ocular infections in patients undergoing microbiological investigations across a multi-tier ophthalmology network in India.

METHODS

This cross-sectional hospital-based study included 15,822 patients in whom microbiology samples were analysed between September 2013 and December 2021. Ocular tissue of patients in whom a microbiology sample was processed in at least one eye were included. The data were collected using an indigenously developed electronic medical record system.

RESULTS

Among the 15,822 patients, bacteria (51.06%) was the most common aetiology followed by fungus (38.27%). The majority of the patients were male (68.10%) and adults (90.01%). The most common age group was during the sixth decade of life with 2,914 (18.42%) patients. The patients were more commonly from the lower socio-economic status (51.61%) and from the rural geography (46.82%). Majority of the specimens sent for microbiological analysis were corneal scrapings (68.61%) followed by vitreous (8.77%). The most common bacteria isolated was Staphylococcus aureus (14.45%) followed by Pseudomonas aeruginosa (12.53%) and among the fungus were Fusarium (30.53%) and Aspergillus species (29.86%). Acanthamoeba (1.26%) and Microsporidia (0.38%) accounted for a minority of the infections in the samples. Fungus (53.10%; p ≤ 0.00001) and virus (51.08%; p = 0.000673) aetiology was found to be significantly higher in patients presenting from the rural geography.

CONCLUSION

The most common aetiology of infection in ocular disease is bacterial but fungal infections also accounted for a significant proportion. The majority of the patients with ocular infections presented from the rural geography and from lower socio-economic status.

Leveraging electronic health records to streamline the conduct of cardiovascular clinical trials

Conventional randomized controlled trials (RCTs) can be expensive, time intensive, and complex to conduct. Trial recruitment, participation, and data collection can burden participants and research personnel. In the past two decades, there have been rapid technological advances and an exponential growth in digitized healthcare data. Embedding RCTs, including cardiovascular outcome trials, into electronic health record systems or registries may streamline screening, consent, randomization, follow-up visits, and outcome adjudication. Moreover, wearable sensors (i.e. health and fitness trackers) provide an opportunity to collect data on cardiovascular health and risk factors in unprecedented detail and scale, while growing internet connectivity supports the collection of patient-reported outcomes. There is a pressing need to develop robust mechanisms that facilitate data capture from diverse databases and guidance to standardize data definitions. Importantly, the data collection infrastructure should be reusable to support multiple cardiovascular RCTs over time. Systems, processes, and policies will need to have sufficient flexibility to allow interoperability between different sources of data acquisition. Clinical research guidelines, ethics oversight, and regulatory requirements also need to evolve. This review highlights recent progress towards the use of routinely generated data to conduct RCTs and discusses potential solutions for ongoing barriers. There is a particular focus on methods to utilize routinely generated data for trials while complying with regional data protection laws. The discussion is supported with examples of cardiovascular outcome trials that have successfully leveraged the electronic health record, web-enabled devices or administrative databases to conduct randomized trials.

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

Implementing Innovation: Informatics-Based Technologies to Improve Care Delivery and Clinical Research

Improving technology has promised to improved health care delivery and the lives of patients. The realized benefits of technology, however, are delayed or less than anticipated. Three recent technology initiatives are reviewed: the Clinical Trials Rapid Activation Consortium (CTRAC), minimal Common Oncology Data Elements (mCODE), and electronic Patient-Reported Outcomes. Each initiative is at a different stage of maturity but promises to improve the delivery of cancer care. CTRAC is an ambitious initiative funded by the National Cancer Institute (NCI) to develop processes across multiple NCI-supported cancer centers to facilitate the development of centralized electronic health record (EHR) treatment plans. Facilitating interoperability of treatment regimens has the potential to improve sharing between centers and decrease the time to begin clinical trials. The mCODE initiative began in 2019 and is currently Standard for Trial Use version 2. This data standard provides an abstraction layer on top of EHR data and has been implemented across more than 60 organizations. Patient-reported outcomes have been shown to improve patient care in numerous studies. Best practices for how to leverage these in an oncology practice continue to evolve. These three examples show how innovative has diffused into practice and evolved cancer care delivery and highlight a movement toward patient-centered data and interoperability.

The Role of Scribes in Orthopaedics

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The rapid increase in the use of electronic medical records (EMRs) has led to some unintended consequences that negatively affect physicians and their patients.

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The use of medical scribes may serve as a possible solution to some of the EMR-related concerns.

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Research has demonstrated an overall positive impact of having scribes on both physician and patient well-being, safety, and satisfaction.

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Adaptation of advances in technology, including remote and asynchronous scribing, use of face-mounted devices, voice recognition software, and applications of artificial intelligence may address some of the barriers to more traditional in-person scribes.

Leveraging Electronic Health Records for Guideline-Based Asthma Documentation

BACKGROUND

Asthma is the most common pediatric chronic disease; thus, clinical guidelines have been developed for its assessment and management, which rely on systematic symptom documentation. Electronic health records (EHR) have the potential to record clinical data systematically; however, variability in documentation persists.

OBJECTIVE

To identify if the use of a structured asthma template is associated with increased guideline-based asthma documentation and clinical outcomes when compared with the use of nonstructured ones.

METHODS

We performed a retrospective case-control study comparing the use of nonstructured templates (NSTs) and asthma-structured templates (ASTs) in new patient and first follow-up encounters, evaluated by pediatric pulmonologists between March 2016 and December 2021. Asthma history items were selected following clinical guidelines, summarized in 29 items for new and 22 items for follow-up encounters. Associations with demographic, spirometry, and health care utilization were explored.

RESULTS

A total of 546 initial encounters were included; 450 used structured templates. The use of an AST was associated with higher documentation of asthma items in initial and follow-up encounters. Linear regression analysis showed that the use of ASTs was associated with a 28.2% and 39.65% increase in asthma history completeness (in initial and follow-up encounters, respectively), compared with the use of NSTs. AST use was associated with higher rates of systemic steroid prescriptions within 12 months. No other differences were observed after adjusting for asthma severity.

CONCLUSIONS

Using asthma-specific structured templates was associated with increased guideline-based asthma documentation. Leveraging the EHR as a clinical and research tool has the potential to improve clinical practice.

Top Ten Tips Palliative Care Clinicians Should Know About Leveraging the Electronic Health Record for Data Collection and Quality Improvement

As palliative care (PC) programs rapidly grow and expand across settings, the need to measure, improve, and standardize high-quality PC has also grown. The electronic health record (EHR) is a key component of these efforts as a central hub of care delivery and a repository of patient and system data. Deliberate efforts to leverage the EHR for PC quality improvement (QI) can help PC programs and health systems improve care for patients with serious illnesses. This article, written by clinicians with experience in QI, informatics, and clinical program development, provides practical tips and guidance on EHR strategies and tools for QI and quality measurement.

Identification of Preanesthetic History Elements by a Natural Language Processing Engine

BACKGROUND

Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider.

METHODS

For each patient, we collected all pertinent notes from the institution's electronic medical record that were available no later than 1 day before their preoperative anesthesia clinic appointment. Pertinent notes included free-text notes consisting of history and physical, consultation, outpatient, inpatient progress, and previous preanesthetic evaluation notes. The free-text notes were processed by a Named Entity Recognition pipeline, an NLP machine learning model trained to recognize and label spans of text that corresponded to medical concepts. These medical concepts were then mapped to a list of medical conditions that were of interest for a preanesthesia evaluation. For each condition, we calculated the percentage of time across all patients in which (1) the NLP pipeline and the anesthesiologist both captured the condition; (2) the NLP pipeline captured the condition but the anesthesiologist did not; and (3) the NLP pipeline did not capture the condition but the anesthesiologist did.

RESULTS

A total of 93 patients were included in the NLP pipeline input. Free-text notes were extracted from the electronic medical record of these patients for a total of 9765 notes. The NLP pipeline and anesthesiologist agreed in 81.24% of instances on the presence or absence of a specific condition. The NLP pipeline identified information that was not noted by the anesthesiologist in 16.57% of instances and did not identify a condition that was noted by the anesthesiologist's review in 2.19% of instances.

CONCLUSIONS

In this proof-of-concept study, we demonstrated that utilization of NLP produced an output that identified medical conditions relevant to preanesthetic evaluation from unstructured free-text input. Automation of risk stratification tools may provide clinical decision support or recommend additional preoperative testing or evaluation. Future studies are needed to integrate these tools into clinical workflows and validate its efficacy.

Nonspecific deidentification of date-like text in deidentified clinical notes enables reidentification of dates

To facilitate the secondary usage of electronic health record data for research, the University of California, San Francisco (UCSF) recently implemented a clinical data warehouse including, among other data, deidentified clinical notes and reports, which are available to UCSF researchers without Institutional Review Board approval. For deidentification of these notes, most of the Health Insurance Portability and Accountability Act identifiers are redacted, but dates are transformed by shifting all dates for a patient back by the same random number of days. We describe an issue in which nonspecific (ie, excess) transformation of nondate, date-like text by this deidentification process enables reidentification of all dates, including birthdates, for certain patients. This issue undercuts the common assumption that excess deidentification is a safe tradeoff to protect patient privacy. We present this issue as a caution to other institutions that may also be considering releasing deidentified notes for research.

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers' Feedback

Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.

Examining the adoption and implementation of behavioral electronic health records by healthcare professionals based on the clinical adoption framework

Background

While various quantitative studies based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Models (TAM) exist in the general medical sectors, just a few have been conducted in the behavioral sector; they have all been qualitative interview-based studies.

Objective

The purpose of this study is to assess the adoption dimensions of a behavioral electronic health record (EHR) system for behavioral clinical professionals using a modified clinical adoption (CA) research model that incorporates a variety of micro, meso, and macro level factors.

Methods

A questionnaire survey with quantitative analysis approach was used via purposive sampling method. We modified the existing CA framework to be suitable for evaluating the adoption of an EHR system by behavioral clinical professionals. We designed and verified questionnaires that fit into the dimensions of the CA framework. The survey was performed in five US behavioral hospitals, and the adoption factors were analyzed using a structural equation analysis.

Results

We derived a total of seven dimensions, omitting those determined to be unsuitable for behavioral clinical specialists to respond to. We polled 409 behavioral clinical experts from five hospitals. As a result, the ease of use and organizational support had a substantial impact on the use of the behavioral EHR system. Although the findings were not statistically significant, information and service quality did appear to have an effect on the system's ease of use. The primary reported benefit of behavioral EHR system adoption was the capacity to swiftly locate information, work efficiently, and access patient information via a mobile app, which resulted in more time for better care. The primary downside, on the other hand, was an unhealthy reliance on the EHR system.

Conclusions

We demonstrated in this study that the CA framework can be a useful tool for evaluating organizational and social elements in addition to the EHR system's system features. Not only the EHR system's simplicity of use, but also organizational support, should be considered for the effective implementation of the behavioral EHR system.

Trial Registration: The study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (IRB No.: B-1904-534-301).

Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care

Background

The US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals. Recently, with attention to opioid-related risks, prescribing has declined. However, clinical experts have countered with concerns that some patients for whom opioid-related benefits outweigh risks may be inappropriately discontinued from opioids. Unfortunately, primary care clinicians lack usable tools to help them partner with their patients in choosing pain treatment options that best balance risks and benefits in the context of patient history, resources, symptoms, and goals. Thus, primary care clinicians and patients would benefit from patient-centered clinical decision support (CDS) for this shared decision-making process.

Methods

The objective of this 3-year project is to study the adaptation and implementation of an existing interoperable CDS tool for pain treatment shared decision making, with tailored implementation support, in new clinical settings in the OneFlorida Clinical Research Consortium. Our central hypothesis is that tailored implementation support will increase CDS adoption and shared decision making. We further hypothesize that increases in shared decision making will lead to improved patient outcomes, specifically pain and physical function. The CDS implementation will be guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. The evaluation will be organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. We will adapt and tailor PainManager, an open source interoperable CDS tool, for implementation in primary care clinics affiliated with the OneFlorida Clinical Research Consortium. We will evaluate the effect of tailored implementation support on PainManager’s adoption for pain treatment shared decision making. This evaluation will establish the feasibility and obtain preliminary data in preparation for a multi-site pragmatic trial targeting the effectiveness of PainManager and tailored implementation support on shared decision making and patient-reported pain and physical function.

Discussion

This research will generate evidence on strategies for implementing interoperable CDS in new clinical settings across different types of electronic health records (EHRs). The study will also inform tailored implementation strategies to be further tested in a subsequent hybrid effectiveness-implementation trial. Together, these efforts will lead to important new technology and evidence that patients, clinicians, and health systems can use to improve care for millions of Americans who suffer from pain and other chronic conditions.

Trial registration

ClinicalTrials.gov, NCT05256394, Registered 25 February 2022.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13012-022-01217-4.

Using Technology to Enhance Communication

Digital communication, facilitated by the rise of the electronic health record and telehealth, has transformed clinical workflow. The communication tools, and the purposes they are being used for, need to account for the benefits, risks, and fault tolerance for each tool. In this article, the authors offer several suggestions on how to approach these important issues. These new digital communication tools open the door to novel care models for connecting patients and providers. Most importantly, the way a message is delivered, not the medium through which it is transmitted, is the key to successful communication.

Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics)

The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.

Clinical decision support in cardiovascular medicine

Despite considerable progress in tackling cardiovascular disease over the past 50 years, many gaps in the quality of care for cardiovascular disease remain. Multiple missed opportunities have been identified at every step in the prevention and treatment of cardiovascular disease, such as failure to make risk factor modifications, failure to diagnose cardiovascular disease, and failure to use proper evidence based treatments. With the digital transformation of medicine and advances in health information technology, clinical decision support (CDS) tools offer promise to enhance the efficiency and effectiveness of delivery of cardiovascular care. However, to date, the promise of CDS delivering scalable and sustained value for patient care in clinical practice has not been realized. This article reviews the evidence on key emerging questions around the development, implementation, and regulation of CDS with a focus on cardiovascular disease. It first reviews evidence on the effectiveness of CDS on healthcare process and clinical outcomes related to cardiovascular disease and design features associated with CDS effectiveness. It then reviews the barriers encountered during implementation of CDS in cardiovascular care, with a focus on unintended consequences and strategies to promote successful implementation. Finally, it reviews the legal and regulatory environment of CDS with specific examples for cardiovascular disease.

Relative Incidence of Emergency Department Visits After Treatment for Prostate Cancer with Radiation Therapy or Radical Prostatectomy

PURPOSE

Side effect profiles play an important role in treatment decisions for localized prostate cancer. Emergency department (ED) visits, which may be due to side effects from treatment, can be measured in real-world, structured, electronic health record (EHR) data. The goal of this study was to determine whether treatments for localized prostate cancer are associated with ED visits, as a measure of side effects, using EHR data.

METHODS AND MATERIALS

We used a self-controlled case series study (SCCS) design, including patients treated at an urban academic medical center with radiation therapy (RT) or radical prostatectomy (RP) for prostate cancer between 2011 and 2020 who had visits documented for ≥ 6 months before and after treatment and ≥1 ED visit. We estimated relative incidences (RI) of ED visits, comparing incidence in the exposed and unexposed periods, with the exposed period being between start of treatment and 1 month after completion, and the unexposed period consisting of all other documented time.

RESULTS

Among men who had at least one ED visit and after adjusting for age, there were higher rates of ED visits after RP (RI 20.4, 95% confidence interval [CI] 15.4-27.0, p<0.001), RT overall (RI 2.4, CI 1.7-3.4, p<0.001), intensity modulated radiation therapy with high dose-rate brachytherapy (HDR) (RI 3.4, CI 1.7-6.8, p<0.001) or stereotactic body radiation therapy boost (RI 7.1, CI 3.4-14.8, p < 0.001), and HDR alone (RI 16.3, CI 7.2-36.9, p<0.001), compared to unexposed time. The number needed to harm to result in an ED visit was less for RP (17, CI 13-23) than RT overall (43, CI 25-126), but varied by RT modality.

CONCLUSIONS

In summary, relative rates of ED visits vary by treatment type, suggesting differing severities of side effects. These data may aid in selecting treatments and demonstrate the feasibility of using the SCCS study design on ED visits in real-world, structured EHR data to better understand side effects of treatment.

Association between state-level malpractice environment and clinician electronic health record (EHR) time

OBJECTIVE

Clinicians spend significant time working in the electronic health record (EHR). The US is an outlier in EHR time, suggesting that EHR-related work may be driven in part by the legal environment and threat of malpractice. To assess this, we evaluate the association between state-level malpractice climate and clinician time spent in the EHR.

MATERIALS AND METHODS

We use EHR metadata from 351 ambulatory care health systems in the United States using Epic from January-August 2019 combined with state-level data on malpractice incidence and payouts. We used descriptive statistics to measure variation in clinician EHR time, including total EHR time, documentation time per day, and after-hours EHR time per day. Multi-variable regression evaluated the association between clinicians in high malpractice states and EHR use.

RESULTS

We found no association between location in a state in the top-quartile of malpractice payouts and time spent in the EHR per day, time spent in the EHR outside of scheduled hours, or time spent documenting per day, except for a subgroup of the clinicians in the highest malpractice specialties, where there was a small increase in EHR time per day (B = 6.08 min, P < 0.001) and time spent documenting notes (B = 2.77 min, P < 0.001).

DISCUSSION

State-level differences in malpractice incidence are unlikely to be a significant driver of EHR work for most clinicians.

CONCLUSION

Policymakers seeking to address EHR documentation burden should examine burden driven by other socio-technical demands on clinician time, such as billing or quality measurement.

The Power of Patient Engagement with Electronic Health Records as Research Participants (Preprint)

Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants.

Progress toward Digital Transformation in an Evolving Post-acute Landscape

Digitization has been a central pillar of structural investments to promote organizational capacity for transformation, and yet skilled nursing facilities (SNFs) and other post-acute providers have been excluded and/or delayed in benefitting from the past decade of substantial public and private sector investment in information technology (IT). These settings have limited internal capacity and resources to invest in digital capabilities on their own, propagating a limited infrastructure that may only further sideline SNFs and their role in an ever-evolving healthcare landscape that needs to be focused on age-friendly, high-value care. Meaningful progress will require continuous refinement of supportive policy, financial investment, and scalable organizational best practices specific to the SNF context. In this essay, we lay out an action agenda to move from age-agnostic to age-friendly digital transformation. Key to the value proposition of these efforts is a focus on interoperability- the seamless exchange of electronic health information across settings that is critical for care coordination and for providers to have the information they need to make safe and appropriate care decisions. Interoperability is not synonymous with digital transformation, but a foundational building block for its potential. We characterize the current state of digitization in SNFs in the context of key health IT policy advancements over the past decade, identifying ongoing and emergent policy work where the digitization needs of SNFs and other post-acute settings can be better addressed. We also discuss accompanying implementation considerations and strategies for optimally translating policy efforts into impactful practice change across an ever-evolving post-acute landscape. Acting on these insights at the policy and practice level provides cautious optimism that nursing home care – and care for older adults across the care continuum – may benefit more equitably from the promise of future digitization.