The Impact of Market Factors on Meaningful Use of Electronic Health Records Among Primary Care Providers: Evidence From Florida Using Resource Dependence Theory and Information Uncertainty Perspective.

BACKGROUND: Using federal funds from the 2009 Health Information Technology for Economic and Clinical Health Act, the Centers for Medicare and Medicaid Services funded the 2011-2021 Medicaid electronic health record (EHR) incentive programs throughout the country. OBJECTIVE: Identify the market factors associated with Meaningful Use (MU) of EHRs after primary care providers (PCPs) enrolled in the Florida-EHR incentives program through Adopting, Improving, or Upgrading (AIU) an EHR technology. RESEARCH DESIGN: Retrospective cohort study using 2011-2018 program records for 8464 Medicaid providers. MAIN OUTCOME: MU achievement after first-year incentives. INDEPENDENT VARIABLES: The resource dependence theory and the information uncertainty perspective were used to generate key-independent variables, including the county's rurality, educational attainment, poverty, health maintenance organization penetration, and number of PCPs per capita. ANALYTICAL APPROACH: All the county rates were converted into 3 dichotomous measures corresponding to high, medium, and low terciles. Descriptive and bivariate statistics were calculated. A generalized hierarchical linear model was used because MU data were clustered at the county level (level 2) and measured at the practice level (level 1). RESULTS: Overall, 41.9% of Florida Medicaid providers achieved MU after receiving first-year incentives. Rurality was positively associated with MU ( P <0.001). Significant differences in MU achievements were obtained when we compared the "high" terciles with the "low" terciles for poverty rates ( P =0.002), health maintenance organization penetration rates ( P =0.02), and number of PCPs per capita ( P =0.01). These relationships were negative. CONCLUSIONS: Policy makers and health care managers should not ignore the contribution of market factors in EHR adoption.

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.

The impact of county-level factors on meaningful use of electronic health records (EHRs) among primary care providers.

This study examines the impact of county-level factors on "meaningful use" (MU) of electronic health records (EHRs) for 8415 primary care providers (PCPs) that enrolled in the Florida Medicaid EHR Incentive Program through adopting, improving, or upgrading (AIU) a certified EHR technology. PCPs received incentive payments at enrollment and if they used their EHRs in meaningful ways; ways that benefit patients and providers alike they received additional payments. We conducted a retrospective cohort study of these providers over the 2011-2018 period while linking their records to other state data. We used the core constructs of the resource dependence theory (RDT), a well-established organization theory in business management, to operationalize the county-level variables. These variables were rurality, poverty, educational attainment, managed care penetration, changes in population, and number of PCPs per capita. The unit of analysis was provider-years. For practical and computational purposes, all the county variables were dichotomized. We used analysis of variance (ANOVA) to test for differences in MU attestation rates across each county variable. Odds ratios and corresponding 95% confidence intervals were derived from pooled logistic regressions using generalized estimated equations (GEE) with the binomial family and logit link functions. Clustered standard errors were used. Approximately 42% of these providers attested to MU after receiving first-year incentives. Rurality and poverty were significantly associated with MU. To some degree, managed care penetration, change in population size, and number of PCPs per capita were also associated with MU. Policy makers and healthcare managers should not ignore the contribution of county-level factors in the diffusion of EHRs among physician practices. These county-level findings provide important insights about EHR diffusion in places where traditionally underserved populations live. This county-perspective is particularly important because of the potential for health IT to enable public health monitoring and population health management that might benefit individuals beyond the patients treated by the Medicaid providers.

Psychometric properties of the Arabic version of the meaningful use of speech scale (Arabic MUSS).

PURPOSE: To examine the construct, discriminative, and predictive validity, and the test-retest reliability of the Arabic Meaningful Use of Speech Scale (MUSS). METHODS: Parents of 102 children with cochlear implantation (CI) with a matching control group of 102 children with normal hearing completed the Arabic-MUSS scale. A random subsample of 30 parents was interviewed after two weeks to examine the test-retest reliability. RESULTS: the construct validity of the Arabic-MUSS was established by exploratory factor analysis that yielded a unidimensional scale and explained a total of 92.48% of the variance in the total score of the Arabic-MUSS. The internal consistency of the scale was excellent with Cronbach's alpha = 0.975. The Arabic-MUSS discriminative validity was supported by the significant difference between the total score of children with CI and children with normal hearing (p < 0.0001). The Arabic-MUSS has a moderate predictive validity as demonstrated by the moderate correlation between the total score and the time since cochlear implantation (p < 0.001). The Arabic-MUSS has excellent test-retest reliability. CONCLUSION: The Arabic-MUSS is a valid and reliable measure that can be used to guide plans for auditory rehabilitation and monitor the progress of children with cochlear implantation over time.IMPLICATIONS FOR REHABILITATIONThe Arabic-Meaningful Use of Speech Scale is a valid and reliable parent-report assessment.The Arabic-Meaningful Use of Speech Scale evaluates functional speech in children with cochlear implantation.Clinicians can use the Arabic- Meaningful Use of Speech Scale to plan and monitor the progress of auditory rehabilitation programs.

The Impact of Meaningful Use and Electronic Health Records on Hospital Patient Safety.

The HITECH Act aimed to leverage Electronic Health Records (EHRs) to improve efficiency, quality, and patient safety. Patient safety and EHR use have been understudied, making it difficult to determine if EHRs improve patient safety. The objective of this study was to determine the impact of EHRs and attesting to Meaningful Use (MU) on Patient Safety Indicators (PSIs). A multivariate regression analysis was performed using a generalized linear model method to examine the impact of EHR use on PSIs. Fully implemented EHRs not attesting to MU had a positive impact on three PSIs, and hospitals that attested to MU had a positive impact on two. Attesting to MU or having a fully implemented EHR were not drivers of PSI-90 composite score, suggesting that hospitals may not see significant differences in patient safety with the use of EHR systems as hospitals move towards pay-for-performance models. Policy and practice may want to focus on defining metrics and PSIs that are highly preventable to avoid penalizing hospitals through reimbursement, and work toward adopting advanced analytics to better leverage EHR data. These findings will assist hospital leaders to find strategies to better leverage EHRs, rather than relying on achieving benchmarks of MU objectives.

The Evolution and Typology of Learning Health System Hospitals and other Health Information Interested Hospitals in the US.

This study identifies the type, distribution, and interactions of US hospitals that identify as electronic-data-driven, patient-centric, and learning-focused. Such facilities, termed Health Information Interested (HII) hospitals in this study, meet the defining criteria for one or more of the following designations: learning health systems (LHS), Health Information Technology for Economic and Clinical Health (HITECH) meaningful use stage three compliant (MU3), Patient-Centered Outcomes Research Institute (PCORI) funded, or medical home/safety net (MH/SN) hospital. The American Hospital Association (AHA) IT supplemental survey and other supporting data spanning 2013 to 2018 were used to identify HII hospitals. HII hospitals increased from 19.9 percent to 62.4 percent of AHA reporting hospitals from 2013 to 2018. HII subcategories in 2018 such as the full LHS (37.2 percent) and MU3 (46.9 percent) were dominant, with 33.2 percent having both designations. This indicates increased interest in patient-centric, learning-focused care using electronic health data. This information can enable health information management (HIM) professionals to be aware of programs or approaches that can facilitate learning-focused, patient-centric care using electronic health data within health systems.

Making Meaningful Use of Price Transparency Data: Describing Price Variation of Spine Surgery and Imaging in a Single System.

Price transparency is an increasingly popular solution for high healthcare expenditures in the United States, but little is known about its potential to facilitate patient price shopping. Our objective was to analyze interhospital and interpayer price variability in spine surgery and spine imaging using newly public payer-specific negotiated charges (PNCs). We selected a subset of billing codes for spine surgery and spine imaging at 12 hospitals within a Saint Louis metropolitan area healthcare system. We then compared PNCs for these procedures and tested for significant differences in interhospital and interinsurer IQR using the Mann-Whitney U Test. We found significantly greater IQRs of PNCs as a factor of the insurance plan than as a factor of the hospital for cervical spinal fusions (interinsurer IQR $8256; interhospital IQR $533; P < .0001), noncervical spinal fusions (interinsurer IQR $28 423; interhospital IQR $5512; P < .001), computed tomographies of the lower spine (interinsurer IQR $595; interhospital IQR $113; P < .0001), and MRIs lower spinal canal (interinsurer IQR $1010; interhospital IQR $158; P < .0001). There was no significant difference between the interinsurer IQR and the interhospital IQR for lower spine x-rays (interinsurer IQR $107; interhospital IQR $67; P = .0543). Despite some between-hospital heterogeneity, we show significantly higher price variability between insurers than between hospitals. Our single system analysis limits our ability to generalize, but our results suggest that savings depend more on hospital and provider negotiations than patient price shopping, given the difficulty of switching insurers.

Do Patient Engagement IT Functionalities Influence Patient Safety Outcomes? A Study of US Hospitals.

OBJECTIVE: Patient engagement using health information technology (IT) functionalities can be a powerful tool in managing their own care for better health outcomes. Therefore, this study explores whether patient engagement IT functionalities and electronic health record (EHR) can affect patient safety outcomes. DESIGN: Using longitudinal study design for general acute care hospitals within the United States, we examine the interaction effects of EHR and patient engagement IT functionalities on patient safety outcomes (adverse incident rate) using a generalized estimating equation. SETTING: Our national sample consisted of 9759 hospital-year observations from 2014 to 2018. Overall, we found a significant association between adverse incident rate and patient engagement level and EHR adoption level. RESULTS: On average, as the combined effects of patient engagement level and EHR adoption level increases, the adverse incident rate decreases by approximately 0.49 (P < .01). Incorporating patient engagement functionalities is becoming an essential tool to improve health outcomes and will play an instrumental role in meeting meaningful use standards. CONCLUSIONS: Our study provides insights into the potential synergy between a hospital's existing EHR maturity and patient engagement health IT functionalities in affecting organizational performance. Organizational culture and capabilities pertinent to adopting patient engagement health IT functionalities infrastructure should be established first to provide the impetus for this synergy.

Meaningful use of imaging resources to rule out cerebral venous sinus thrombosis after ChAdOx1 COVID-19 vaccination: Evaluation of the AHA diagnostic algorithm with a clinical cohort and a systematic data review.

Vaccine-induced immune thrombotic thrombocytopenia (VITT) with cerebral venous thrombosis (CVST) is an improbable (0.0005%), however potentially lethal complication after ChAdOx1 vaccination. On the other hand, headache is among the most frequent side effects of ChAdOx1 (29.3%). In September 2021, the American Heart Association (AHA) suggested a diagnostic workflow to facilitate risk-adapted use of imaging resources for patients with neurological symptoms after ChAdOx1. We aimed to evaluate the AHA workflow in a retrospective patient cohort presenting at four primary care hospitals in Germany for neurological complaints after ChAdOx1. Scientific literature was screened for case reports of VITT with CVST after ChAdOx1, published until September 1st, 2021. One-hundred-thirteen consecutive patients (77 female, mean age 38.7 +/- 11.9 years) were evaluated at our institutes, including one case of VITT with CVST. Further 228 case reports of VITT with CVST are published in recent literature, which share thrombocytopenia (225/227 reported) and elevated d-dimer levels (100/101 reported). The AHA workflow would have recognized all VITT cases with CVST (100% sensitivity), the number needed to diagnose (NND) was 1:113. Initial evaluation of thrombocytopenia or elevated d-dimer levels would have lowered the NND to 1:68, without cost of sensitivity. Hence, we suggest that in case of normal thrombocyte and d-dimer levels, the access to further diagnostics should be limited by the established clinical considerations regardless of vaccination history.

Meaningful patient and public involvement in digital health innovation, implementation and evaluation: A systematic review.

INTRODUCTION: The importance of meaningfully involving patients and the public in digital health innovation is widely acknowledged, but often poorly understood. This review, therefore, sought to explore how patients and the public are involved in digital health innovation and to identify factors that support and inhibit meaningful patient and public involvement (PPI) in digital health innovation, implementation and evaluation. METHODS: Searches were undertaken from 2010 to July 2020 in the electronic databases MEDLINE, EMBASE, PsycINFO, CINAHL, Scopus and ACM Digital Library. Grey literature searches were also undertaken using the Patient Experience Library database and Google Scholar. RESULTS: Of the 10,540 articles identified, 433 were included. The majority of included articles were published in the United States, United Kingdom, Canada and Australia, with representation from 42 countries highlighting the international relevance of PPI in digital health. 112 topic areas where PPI had reportedly taken place were identified. Areas most often described included cancer (n = 50), mental health (n = 43), diabetes (n = 26) and long-term conditions (n = 19). Interestingly, over 133 terms were used to describe PPI; few were explicitly defined. Patients were often most involved in the final, passive stages of an innovation journey, for example, usability testing, where the ability to proactively influence change was severely limited. Common barriers to achieving meaningful PPI included data privacy and security concerns, not involving patients early enough and lack of trust. Suggested enablers were often designed to counteract such challenges. CONCLUSIONS: PPI is largely viewed as valuable and essential in digital health innovation, but rarely practised. Several barriers exist for both innovators and patients, which currently limits the quality, frequency and duration of PPI in digital health innovation, although improvements have been made in the past decade. Some reported barriers and enablers such as the importance of data privacy and security appear to be unique to PPI in digital innovation. Greater efforts should be made to support innovators and patients to become meaningfully involved in digital health innovations from the outset, given its reported benefits and impacts. Stakeholder consensus on the principles that underpin meaningful PPI in digital health innovation would be helpful in providing evidence-based guidance on how to achieve this. PATIENT OR PUBLIC CONTRIBUTION: This review has received extensive patient and public contributions with a representative from the Patient Experience Library involved throughout the review's conception, from design (including suggested revisions to the search strategy) through to article production and dissemination. Other areas of patient and public contributor involvement include contributing to the inductive thematic analysis process, refining the thematic framework and finalizing theme wording, helping to ensure relevance, value and meaning from a patient perspective. Findings from this review have also been presented to a variety of stakeholders including patients, patient advocates and clinicians through a series of focus groups and webinars. Given their extensive involvement, the representative from the Patient Experience Library is rightly included as an author of this review.

Observing Provider Utilization of Electronic Health Records to Improve Clinical Quality Metrics.

Introduction: This study compared changes of healthcare quality in a Michigan Medicaid population before and after physician adoption of electronic health records (EHRs) via the Meaningful Use (MU) program for selected Healthcare Effectiveness Data and Information Set (HEDIS) quality of care measures. Methods: Healthcare measures included well-child visits, cancer screening, and chronic illness quality measures. Utilization data were obtained from Medicaid paid claims and encounter data with providers (N=291) receiving their first MU incentive in 2014 and at least one HEDIS-defined outpatient visit with a Michigan Medicaid enrollee. Paired t-tests with a repeated measures design were utilized to analyze the data. Results: Improvements in quality of infant well-child visits (mean difference = 10.2) and colorectal cancer screening (mean difference = 8.0 percent) were observed. We found no change or slight decreases for the other selected measures. Conclusion: These outcomes inform the performance and ability of EHRs to improve quality of healthcare standards particularly as technology continues to evolve under the Centers for Medicare & Medicaid Services (CMS) Interoperability and Patient Access final rule.

Meaningful use of a digital platform and structured telephone support to facilitate remote person-centred care - a mixed-method study on patient perspectives.

BACKGROUND: Process evaluations are useful in clarifying results obtained from randomised controlled trials (RCTs). Traditionally, the degree of intervention usage in process evaluations is monitored by measuring dose or evaluating implementation fidelity. From a person-centred perspective, such evaluations should be supplemented with patients' experiences of meaningful use, given that intervention use should be agreed upon between interested parties and tailored to each patient. This study aimed to elucidate patients' experiences of a remote person-centred care (PCC) intervention by deepening the understanding of, if, how and for whom the intervention contributed to meaningful use. METHODS: Patients (n = 86) were recruited from the RCT PROTECT intervention group. A convergent mixed-method approach was implemented. Data were collected in parallel with the ongoing RCT via a survey, including ratings and written comments on meaningful or non-meaningful use. Also, interviews were performed with twelve purposefully selected participants. Descriptive statistics, logistic regression and content analysis were employed. Data sources were integrated in the results. RESULTS: Most participants rated the overall intervention as meaningful to use, with the telephone support rated as most meaningful. Interviews and written comments showed that patient ratings on meaningful use were explained by four categories: Not in need, Communication deficiency, Benefits in everyday life and A personal boost. Meaningful use of rating symptoms on the digital platform was predicted by living alone (adjusted odds ratio [aOR] = 2.8 P = .044). A diagnosis of chronic obstructive pulmonary disease (COPD) predicted meaningful use of digital platform direct messaging (aOR = 3.5, P = .045). Moreover, having access to direct-dial telephone contact explained meaningful use among participants with low ratings of technical competence (aOR = 3.6, P = .014). CONCLUSIONS: The combined digital platform and structured telephone support could be helpful in identifying preventive actions to maintain health for people diagnosed with COPD and chronic heart failure but tends to be more meaningful for those diagnosed with COPD. Overall, lower adoption of the digital platform was seen compared to telephone support. Shortcomings were noted in the digital platform's implementation that negatively influences experiences of meaningful use. When used, the intervention proved to be an easily applicable and valued tool to support preventive actions in a person-centred manner.

Addressing psychiatric disorders and genetics: the meaningful use of comics for health information.

In the fields of healthcare and education, comics have shown considerable academic and teaching importance, with their combination of text and images. As a well-known character in comics, Batman allows us to discuss several relevant topics, from the outcomes in adulthood of experiencing parental death in childhood to genetic and epigenetic changes that may be factors of vulnerability to psychiatric disorders in adulthood. In this text, a narrative of Batman is developed, demonstrating the possibility of using it in an academic context and intending at paying greater attention to patients' history and the relationship between genetics and behaviour.

The Impact of Electronic Health Records and Meaningful Use on Inpatient Quality.

ABSTRACT: It is unclear if national investments of the HITECH Act have resulted in significant improvements in care processes and outcomes by making "Meaningful Use (MU)" of Electronic Health Record (EHR) systems. The objective of this study is to determine the impact of EHRs and MU on inpatient quality. We used inpatient hospitalization data, American Hospital Association annual survey, and the Centers for Medicare and Medicaid Services attestation records to study the impact of EHRs on inpatient quality composite scores. Agency for Healthcare Research and Quality Inpatient Quality Indicator (IQI) software version 5.0 was used to compute the hospital-level risk-adjusted standardized rates for IQI indicators and composite scores. After adjusting for confounding factors, EHRs that attested to MU had a positive impact on IQI 90 and IQI 91 composite scores with an 8% decrease in composites for mortality for selected procedures and 18% decrease in composites for mortality for selected conditions. Meaningful Use attestation may be an important driver related to inpatient quality. Health care leaders may need to focus on quality improvement initiatives and advanced analytics to better leverage their EHRs to improve IQI 90 composite score for mortality for selected procedures, because we observed a lesser impact on IQI 90 compared with IQI 91.

Discrete Heart Rate Values or Continuous Streams? Representation, Variability, and Meaningful Use of Vital Sign Data.

Documentation and review of patient heart rate are a fundamental process across a myriad of clinical settings. While historically recorded manually, bedside monitors now provide for the automated collection of such data. Despite the availability of continuous streaming data, patients' charts continue to reflect only a subset of this information as snapshots recorded throughout a hospitalization. Over the past decade, prominent works have explored the implications of such practices and established fundamental differences in the alignment of discrete charted vitals and steaming data captured by monitoring systems. Limited work has examined the temporal properties of these differences, how they manifest, and their relation to clinical applications. The work presented in this article addresses this disparity, providing evidence that differences between charting techniques extend to measures of variability. Our results demonstrate how variability manifests with respect to temporal elements of charting timing and how it can facilitate personalized care by contextualizing deviations in magnitude. This work also highlights the utility of variability metrics with relation to clinical measures including associations to severity scores and a case study utilizing complex variability metrics derived from the complete set of monitor data.

Utilization and uptake of the UpToDate clinical decision support tool at the Makerere University College of Health Sciences (MakCHS), Uganda.

BACKGROUND: The use of point-of-care, evidence-based tools is becoming increasingly popular. They can provide easy-touse, high-quality information which is regularly updated and has been shown to improve clinical outcomes. Integrating such tools into clinical practice is an important component of improving the quality of health care. However, because such tools are rarely used in resource-limited settings, there is limited research on uptake especially among medical students. OBJECTIVE: This paper explores the uptake of one such tool, Up-To-Date, when provided free of cost at a medical school in Africa. METHODS: In partnership with the Better Evidence at Ariadne Labs free access to UpToDate was granted through the MakCHS IP address. On-site librarians facilitated training sessions and spread awareness of the tool. Usage data was aggregated, based on log ins and content views, presented and analyzed using Excel tables and graphs. RESULTS: The data shows evidence of meaningful usage, with 43,043 log ins and 15,591 registrations between August 2019 and August 2020. The most common topics viewed were in obstetrics and gynecology, pediatrics, drug information, and infectious diseases. Access occurred mainly through the mobile phone app. CONCLUSION: Findings show usage by various user categories, but with inconsistent uptake and low usage. Librarians can draw upon these results to encourage institutions to support uptake of point-of-care tools in clinical practice.

Analyzing Patient Secure Messages Using a Fast Health Care Interoperability Resources (FIHR)-Based Data Model: Development and Topic Modeling Study.

BACKGROUND: Patient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children's Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care. OBJECTIVE: This study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information. METHODS: We initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently. RESULTS: The proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model. CONCLUSIONS: Our data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients' needs and improve patient-centered care.

Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant.

Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).