Use of electronic health records in U.S. hospitals

Assessing Patient Trust in Automation in Health Care Systems: Within-Subjects Experimental Study

BACKGROUND

Health care technology has the ability to change patient outcomes for the betterment when designed appropriately. Automation is becoming smarter and is increasingly being integrated into health care work systems.

OBJECTIVE

This study focuses on investigating trust between patients and an automated cardiac risk assessment tool (CRAT) in a simulated emergency department setting.

METHODS

A within-subjects experimental study was performed to investigate differences in automation modes for the CRAT: (1) no automation, (2) automation only, and (3) semiautomation. Participants were asked to enter their simulated symptoms for each scenario into the CRAT as instructed by the experimenter, and they would automatically be classified as high, medium, or low risk depending on the symptoms entered. Participants were asked to provide their trust ratings for each combination of risk classification and automation mode on a scale of 1 to 10 (1=absolutely no trust and 10=complete trust).

RESULTS

Results from this study indicate that the participants significantly trusted the semiautomation condition more compared to the automation-only condition (P=.002), and they trusted the no automation condition significantly more than the automation-only condition (P=.03). Additionally, participants significantly trusted the CRAT more in the high-severity scenario compared to the medium-severity scenario (P=.004).

CONCLUSIONS

The findings from this study emphasize the importance of the human component of automation when designing automated technology in health care systems. Automation and artificially intelligent systems are becoming more prevalent in health care systems, and this work emphasizes the need to consider the human element when designing automation into care delivery.

The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports

Purpose: This study evaluates the efficacy of a commercial medical Named Entity Recognition (NER) model combined with a post-processing protocol in identifying incidental pulmonary nodules from CT reports. Methods: We analyzed 9165 anonymized CT reports and classified them into 3 categories: no nodules, nodules present, and nodules >6 mm. For each report, a generic medical NER model annotated entities and their relations, which were then filtered through inclusion/exclusion criteria selected to identify pulmonary nodules. Ground truth was established by manual review. To better understand the relationship between model performance and nodule prevalence, a subset of the data was programmatically balanced to equalize the number of reports in each class category. Results: In the unbalanced subset of the data, the model achieved a sensitivity of 97%, specificity of 99%, and accuracy of 99% in detecting pulmonary nodules mentioned in the reports. For nodules >6 mm, sensitivity was 95%, specificity was 100%, and accuracy was 100%. In the balanced subset of the data, sensitivity was 99%, specificity 96%, and accuracy 97% for nodule detection; for larger nodules, sensitivity was 94%, specificity 99%, and accuracy 98%. Conclusions: The NER model demonstrated high sensitivity and specificity in detecting pulmonary nodules reported in CT scans, including those >6 mm which are potentially clinically significant. The results were consistent across both unbalanced and balanced datasets indicating that the model performance is independent of nodule prevalence. Implementing this technology in hospital systems could automate the identification of at-risk patients, ensuring timely follow-up and potentially reducing missed or late-stage cancer diagnoses.

Measuring cognitive effort using tabular transformer-based language models of electronic health record-based audit log action sequences

OBJECTIVES

To develop and validate a novel measure, action entropy, for assessing the cognitive effort associated with electronic health record (EHR)-based work activities.

MATERIALS AND METHODS

EHR-based audit logs of attending physicians and advanced practice providers (APPs) from four surgical intensive care units in 2019 were included. Neural language models (LMs) were trained and validated separately for attendings' and APPs' action sequences. Action entropy was calculated as the cross-entropy associated with the predicted probability of the next action, based on prior actions. To validate the measure, a matched pairs study was conducted to assess the difference in action entropy during known high cognitive effort scenarios, namely, attention switching between patients and to or from the EHR inbox.

RESULTS

Sixty-five clinicians performing 5 904 429 EHR-based audit log actions on 8956 unique patients were included. All attention switching scenarios were associated with a higher action entropy compared to non-switching scenarios (P < .001), except for the from-inbox switching scenario among APPs. The highest difference among attendings was for the from-inbox attention switching: Action entropy was 1.288 (95% CI, 1.256-1.320) standard deviations (SDs) higher for switching compared to non-switching scenarios. For APPs, the highest difference was for the to-inbox switching, where action entropy was 2.354 (95% CI, 2.311-2.397) SDs higher for switching compared to non-switching scenarios.

DISCUSSION

We developed a LM-based metric, action entropy, for assessing cognitive burden associated with EHR-based actions. The metric showed discriminant validity and statistical significance when evaluated against known situations of high cognitive effort (ie, attention switching). With additional validation, this metric can potentially be used as a screening tool for assessing behavioral action phenotypes that are associated with higher cognitive burden.

CONCLUSION

An LM-based action entropy metric-relying on sequences of EHR actions-offers opportunities for assessing cognitive effort in EHR-based workflows.

It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

Design and evaluation of a web-based electronic health record for amblyopia

Introduction

Amblyopia, or lazy eye, is a type of visual impairment in which the eyesight is not complete, even with the use of glasses. For the treatment of this disease, accurate and continuous examinations are needed. Nowadays, patient-centered care, by relying on web-based electronic records for amblyopia, has the potential to reduce treatment costs, increase the quality of care, and improve the safety and effectiveness of treatment. Therefore, the purpose of this study is to design and evaluate an Electronic Health Record (EHR) for patients with amblyopia.

Methods

The present study is applied developmental research. Using a Morgan table as a sampling tool, a straightforward random sampling technique selected 150 records from 1,500 records that were free of flaws. The design of the electronic version proceeded in a cascading manner so that after the design of each part, it was presented to the amblyopia experts, and if approved, the next part was designed. To design this EHR, the C# programming language and MySQL database were used. A system evaluation was performed by entering and recording patient information. For this purpose, the standard Questionnaire of User Interaction Satisfaction (QUIS), consisting of 18 questions, was used.

Results

According to the amblyopia EHR data elements, the data of physician and patient, examinations, website members, and members' roles were determined. After defining the fields and classes that explain the tables, the EHR was designed. The usability evaluation of the system showed that the mean selection of very good and good options by the users of EHRs was over 90%, indicating the patients' acceptance of web-based EHRs.

Conclusion

The design of an EHR for amblyopia is an effective step toward integrating and improving the information management of these patients. It will also enable the storage and retrieval of patients' information to reduce and facilitate the control of amblyopia complications.

Systematic Review and Meta-Analysis of the Financial Impact of 30-Day Readmissions for Selected Medical Conditions: A Focus on Hospital Quality Performance

BACKGROUND

The Patient Protection and Affordable Care Act (ACA) established the Hospital Quality Initiative in 2010 to enhance patient safety, reduce hospital readmissions, improve quality, and minimize healthcare costs. In response, this study aims to systematically review the literature and conduct a meta-analysis to estimate the average cost of procedure-specific 30-day risk-standardized unplanned readmissions for Acute Myocardial Infarction (AMI), Heart Failure (HF), Pneumonia, Coronary Artery Bypass Graft (CABG), and Total Hip Arthroplasty and/or Total Knee Arthroplasty (THA/TKA).

METHODS

Eligibility Criteria: This study included English language original research papers from the USA, encompassing various study designs. Exclusion criteria comprise studies lacking empirical evidence on hospital financial performance.

INFORMATION SOURCES

A comprehensive search using relevant keywords was conducted across databases from January 1990 to December 2019 (updated in March 2021), covering peer-reviewed articles and gray literature. Risk of Bias: Bias in the included studies was assessed considering study design, adjustment for confounding factors, and potential effect modifiers.

SYNTHESIS OF RESULTS

The review adhered to PRISMA guidelines. Employing Monte Carlo simulations, a meta-analysis was conducted with 100,000 simulated samples. Results indicated mean 30-day readmission costs: USD 16,037.08 (95% CI, USD 15,196.01-16,870.06) overall, USD 6852.97 (95% CI, USD 6684.44-7021.08) for AMI, USD 9817.42 (95% CI, USD 9575.82-10,060.43) for HF, and USD 21,346.50 (95% CI, USD 20,818.14-21,871.85) for THA/TKA.

DISCUSSION

Despite the financial challenges that hospitals face due to the ACA and the Hospital Readmissions Reduction Program, this meta-analysis contributes valuable insights into the consistent cost trends associated with 30-day readmissions.

CONCLUSIONS

This systematic review and meta-analysis provide comprehensive insights into the financial implications of 30-day readmissions for specific medical conditions, enhancing our understanding of the nexus between healthcare quality and financial performance.

Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation

Electronic health records (EHRs) have been increasingly adopted in clinical practices across the United States, providing a primary source of data for clinical research, particularly observational cohort studies. EHRs are a high-yield, low-maintenance source of longitudinal real-world data for large patient populations and provide a wealth of information and clinical contexts that are useful for clinical research and translation into practice. Despite these strengths, it is important to recognize the multiple limitations and challenges related to the use of EHR data in clinical research. Missing data are a major source of error and biases and can affect the representativeness of the cohort of interest, as well as the accuracy of the outcomes and exposures. Here, we aim to provide a critical understanding of the types of data available in EHRs and describe the impact of data heterogeneity, quality, and generalizability, which should be evaluated prior to and during the analysis of EHR data. We also identify challenges pertaining to data quality, including errors and biases, and examine potential sources of such biases and errors. Finally, we discuss approaches to mitigate and remediate these limitations. A proactive approach to addressing these issues can help ensure the integrity and quality of EHR data and the appropriateness of their use in clinical studies.

A framework for inferring and analyzing pharmacotherapy treatment patterns

BACKGROUND

To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data.

METHODS

We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse.

RESULTS

Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change.

CONCLUSIONS

For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment.

Clinician Needs for Electronic Health Record Pediatric and Adolescent Weight Management Tools: A Mixed-Methods Study

BACKGROUND

 Clinicians play an important role in addressing pediatric and adolescent obesity, but their effectiveness is restricted by time constraints, competing clinical demands, and the lack of effective electronic health record (EHR) tools. EHR tools are rarely developed with provider input.

OBJECTIVES

 We conducted a mixed method study of clinicians who provide weight management care to children and adolescents to determine current barriers for effective care and explore the role of EHR weight management tools to overcome these barriers.

METHODS

 In this mixed-methods study, we conducted three 1-hour long virtual focus groups at one medium-sized academic health center in Missouri and analyzed the focus group scripts using thematic analysis. We sequentially conducted a descriptive statistical analysis of a survey emailed to pediatric and family medicine primary care clinicians (n = 52) at two private and two academic health centers in Missouri.

RESULTS

 Surveyed clinicians reported that they effectively provided health behavior lifestyle counseling at well-child visits (mean of 60 on a scale of 1-100) and child obesity visits (63); however, most felt the current health care system (27) and EHR tools (41) do not adequately support pediatric weight management. Major themes from the clinician focus groups were that EHR weight management tools should display data in a way that (1) improves clinical efficiency, (2) supports patient-centered communication, (3) improves patient continuity between visits, and (4) reduces documentation burdens. An additional theme was (5) clinicians trust patient data entered in real time over patient recalled data.

CONCLUSION

 Study participants report that the health care system status quo and currently available EHR tools do not sufficiently support clinicians working to manage pediatric or adolescent obesity and provide health behavior counseling. Clinician input in the development and testing of EHR weight management tools provides opportunities to address barriers, inform content, and improve efficiencies of EHR use.

Electronic health record-supported implementation of an evidence-based pathway for perioperative surgical care

OBJECTIVES

Enhanced recovery pathways (ERPs) are evidence-based approaches to improving perioperative surgical care. However, the role of electronic health records (EHRs) in their implementation is unclear. We examine how EHRs facilitate or hinder ERP implementation.

MATERIALS AND METHODS

We conducted interviews with informaticians and clinicians from US hospitals participating in an ERP implementation collaborative. We used inductive thematic analysis to analyze transcripts and categorized hospitals into 3 groups based on process measure adherence. High performers exhibited a minimum 80% adherence to 6 of 9 metrics, high improvers demonstrated significantly better adherence over 12 months, and strivers included all others. We mapped interrelationships between themes using causal loop diagrams.

RESULTS

We interviewed 168 participants from 8 hospitals and found 3 thematic clusters: (1) "EHR difficulties" with the technology itself and contextual factors related to (2) "EHR enablers," and (3) "EHR barriers" in ERP implementation. Although all hospitals experienced issues, high performers and improvers successfully integrated ERPs into EHRs through a dedicated multidisciplinary team with informatics expertise. Strivers, while enacting some fixes, were unable to overcome individual resistance to EHR-supported ERPs.

DISCUSSION AND CONCLUSION

We add to the literature describing the limitations of EHRs' technological capabilities to facilitate clinical workflows. We illustrate how organizational strategies around engaging motivated clinical teams with informatics training and resources, especially with dedicated technical support, moderate the extent of EHRs' support to ERP implementation, causing downstream effects for hospitals to transform technological challenges into care-improving opportunities. Early and consistent involvement of informatics expertise with frontline EHR clinician users benefited the efficiency and effectiveness of ERP implementation and sustainability.

Analysis of Publications on Health Information Management Using the Science Mapping Method: A Holistic Perspective

OBJECTIVE

In the age of digital transformation, there is a need for a sustainable information management vision in health. Understanding the accumulation of health information management (HIM) knowledge from the past to the present and building a new vision to meet this need reveals the importance of understanding the available scientific knowledge. With this research, it is aimed to examine the scientific documents of the last 40 years of HIM literature with a holistic approach using science mapping techniques and to guide future research.

METHODS

This study used a bibliometric analysis method for science mapping. Co-citation and co-occurrence document analyses were performed on 630 academic publications selected from the Web of Science core collection (WoSCC) database using the keyword "Health Information Management" and inclusion criteria. The analyses were performed using the R-based software Bibliometrix (Version 4.0; K-Synth Srl), Python (Version 3.12.1; The Python Software Foundation), and Microsoft® Excel® 2016.

RESULTS

Co-occurrence analyses revealed the themes of personal health records, clinical coding and data quality, and health information management. The HIM theme consisted of five subthemes: "electronic records", "medical informatics", "e-health and telemedicine", "health education and awareness", and "health information systems (HISs)". As a result of the co-citation analysis, the prominent themes were technology acceptance, standardized clinical coding, the success of HISs, types of electronic records, people with HIM, health informatics used by consumers, e-health, e-mobile health technologies, and countries' frameworks and standards for HISs.

CONCLUSIONS

This comprehensive bibliometric study shows that structured information can be helpful in understanding research trends in HIM. This study identified critical issues in HIM, identified meaningful themes, and explained the topic from a holistic perspective for all health system actors and stakeholders who want to work in the field of HIM.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Fair patient model: Mitigating bias in the patient representation learned from the electronic health records

OBJECTIVE

To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models.

METHODS

We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks.

RESULTS

FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines.

CONCLUSION

FPM is a novel method to pre-train fair and unbiased patient representations from the EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where fairness is important.

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.

"Everything's so Role-Specific": VA Employee Perspectives' on Electronic Health Record (EHR) Transition Implications for Roles and Responsibilities

BACKGROUND

Electronic health record (EHR) transitions are increasingly widespread and often highly disruptive. It is imperative we learn from past experiences to anticipate and mitigate such disruptions. Veterans Affairs (VA) is undergoing a large-scale transition from its homegrown EHR (CPRS/Vista) to a commercial EHR (Cerner), creating a unique opportunity of shedding light on large-scale EHR-to-EHR transition challenges.

OBJECTIVE

To explore one facet of the organizational impact of VA's EHR transition: its implications for employees' roles and responsibilities at the first VA site to implement Cerner Millennium EHR.

DESIGN

As part of a formative evaluation of frontline staff experiences with VA's EHR transition, we conducted brief (~ 15 min) and full-length interviews (~ 60 min) with clinicians and staff at Mann-Grandstaff VA Medical Center in Spokane, WA, before, during, and after transition (July 2020-November 2021).

PARTICIPANTS

We conducted 111 interviews with 26 Spokane clinicians and staff, recruited via snowball sampling.

APPROACH

We conducted audio interviews using a semi-structured guide with grounded prompts. We coded interview transcripts using a priori and emergent codes, followed by qualitative content analysis.

KEY RESULTS

Unlike VA's previous EHR, Cerner imposes additional restrictions on access to its EHR functionality based upon "roles" assigned to users. Participants described a mismatch between established institutional duties and their EHR permissions, unanticipated changes in scope of duties brought upon by the transition, as well as impediments to communication and collaboration due to different role-based views.

CONCLUSIONS

Health systems should anticipate substantive impacts on professional workflows when EHR role settings do not reflect prior workflows. Such changes may increase user error, dissatisfaction, and patient care disruptions. To mitigate employee dissatisfaction and safety risks, health systems should proactively plan for and communicate about expected modifications and monitor for unintended role-related consequences of EHR transitions, while vendors should ensure accurate role configuration and assignment.

Adoption and use of various health information exchange methods for sending inside health information in US hospitals

BACKGROUND

Hospitals may adopt various information transmission methods to participate in health information exchange (HIE) programs. However, even if they adopt multiple mechanisms, they may not actively use all of them to send inside information. This study analyzes the frequently used methods for sending data and the common barriers blocking sending practices in hospital settings.

METHODS

Our sample included 3,015 community hospitals that reported their methods of sending patient health information in the 2019 American Hospital Association Information Technology Supplement Survey. The relationship between obstacles hospitals experienced and their use of the information-sending method was analyzed by using robust Poisson regression models.

RESULTS

Many-to-many exchanges that involve intermediaries such as a health information service provider (HISP), electronic health record (EHR) vendor-based network, and national network, once adopted, were more often used by hospitals than one-to-one exchange methods such as provider portals and direct access to EHR by login credentials. Hospitals that lacked the technical capability to electronically send patient health information were less likely to use any of the methods (p <.01), while hospitals located in a more concentrated market were more likely to send information to outside providers by using provider portal, interface connection and national network (p <.01).

DISCUSSION

There is still a notable gap between hospitals' adoption and the actual use of different HIE methods to send inside information to outside organizations. Results argue that even if hospitals adopted an HIE method, they might not necessarily participate in the actual sharing of information, and the method may remain unused due to several usage barriers.

CONCLUSION

Hospital and market-level barriers associated with using one-to-one and many-to-many HIE methods for sharing information may affect progress in interoperability. Examining the barriers to using multiple HIE methods and their impact on interoperability could offer implications for health information technology (IT) policy and inform health system leaders.

Characteristics of short-term acute care hospitals that experienced a ransomware attack from 2016 to 2021

As cybercrime increasingly targets the health care sector, hospitals face the growing threat of ransomware attacks. Ransomware is a type of malicious software that prevents users from accessing their electronic systems-demanding payment to restore access. In response, momentum is gathering to enact policy that will help hospitals strengthen their cybersecurity defenses. However, to design effective policy, it is crucial to understand the characteristics of hospitals associated with the risk of ransomware attack. In this paper, we compare the characteristics of ransomware-attacked and non-attacked short-term acute care hospitals in the United States. Using data from the American Hospital Association's Annual Survey and the Healthcare Cost Report Information System, we found that ransomware-attacked hospitals were larger, had higher net operating revenue, were more likely to be financially profitable, and more likely to provide trauma, emergency, and obstetric care than non-attacked hospitals. Measures of information technology sophistication did not vary between ransomware-attacked and non-attacked hospitals. These results can be used to tailor policy interventions in order to most effectively respond to and prevent cybercrime in health care.

Toward a stable and low-resource PLM-based medical diagnostic system via prompt tuning and MoE structure

Machine learning (ML) has been extensively involved in assistant disease diagnosis and prediction systems to emancipate the serious dependence on medical resources and improve healthcare quality. Moreover, with the booming of pre-training language models (PLMs), the application prospect and promotion potential of machine learning methods in the relevant field have been further inspired. PLMs have recently achieved tremendous success in diverse text processing tasks, whereas limited by the significant semantic gap between the pre-training corpus and the structured electronic health records (EHRs), PLMs cannot converge to anticipated disease diagnosis and prediction results. Unfortunately, establishing connections between PLMs and EHRs typically requires the extraction of curated predictor variables from structured EHR resources, which is tedious and labor-intensive, and even discards vast implicit information.In this work, we propose an Input Prompting and Discriminative language model with the Mixture-of-experts framework (IPDM) by promoting the model's capabilities to learn knowledge from heterogeneous information and facilitating the feature-aware ability of the model. Furthermore, leveraging the prompt-tuning mechanism, IPDM can inherit the impacts of the pre-training in downstream tasks exclusively through minor modifications. IPDM remarkably outperforms existing models, proved by experiments on one disease diagnosis task and two disease prediction tasks. Finally, experiments with few-feature and few-sample demonstrate that IPDM achieves significant stability and impressive performance in predicting chronic diseases with unclear early-onset characteristics or sudden diseases with insufficient data, which verifies the superiority of IPDM over existing mainstream methods, and reveals the IPDM can powerfully address the aforementioned challenges via establishing a stable and low-resource medical diagnostic system for various clinical scenarios.

Good practices for clinical data warehouse implementation: A case study in France

Real-world data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern clinical data warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema, and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multicentric data reuses as well as innovations in routine care.

Decreasing Racial Disparities in Preventable Emergency Department Visits Through Hospital Health Information Technology Patient Engagement Functionalities

Introduction: Hospitals are a major source of care for underserved populations in the United States. However, little is known about how hospital-based health information technology (HIT) can improve the efficiency of care and reduce disparities. Objective: We examined the variation of preventable emergency department (ED) visits and associated racial disparities by hospital adoption of HIT patient engagement (HIT-PE) functionalities. Methods: This was an observational study of 6,543,514 non-Hispanic Black (Black) and non-Hispanic White (White) adult patients using 2019 datasets of seven states (Arizona, Florida, Kentucky, Maryland, North Carolina, Vermont, Wisconsin) from the State Emergency Department Databases, American Hospital Association Annual Survey & Information Technology Supplement, and Area Health Resources File. Results: High HIT-PE adoption was associated with lower rates of preventable ED (odds ratio [OR] = 0.992, p < 0.001). Specific HIT-PE functions such as importing medical records from other organizations into the patient portal (OR = 0.977, p < 0.001), electronically sending medical information to a third party (OR = 0.970, p < 0.001), and scheduling appointments online (OR = 0.987, p < 0.001) were also associated with reduced preventable ED rates. Black patients had higher rates of preventable ED compared with Whites (OR = 1.386, p < 0.001); however, the interaction of Black patients and high HIT-PE adoption was associated with lower rates of preventable ED (OR = 0.977, p < 0.001). Our results also showed that higher HIT-PE adoption was associated with a reduction in preventable ED visits among Black patients with comorbidities and Black patients living in low-income areas. Conclusions: The results of our study suggest that there is potential to reduce preventable ED rates and racial disparities through hospital-based HIT-PE functionalities.

The Electronic Health Record as a Quality Improvement Tool: Exceptional Potential with Special Considerations

The electronic health record (EHR) offers an exciting opportunity for quality improvement efforts. An understanding of the nuances of a site's EHR landscape including the best practices in clinical decision support design, basics of data capture, and acknowledgment of the potential unintended consequences of technology change is essential to ensuring effective usage of this powerful tool.

Enriching representation learning using 53 million patient notes through human phenotype ontology embedding

The Human Phenotype Ontology (HPO) is a dictionary of >15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from >1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass current computational models. Furthermore, our embedding technique exhibits a high degree of agreement with domain experts' judgment. By transforming complex and multidimensional phenotypes from the HPO format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction.

Co-design of human-centered, explainable AI for clinical decision support

eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models, and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique, and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback, with a two-fold outcome: first, we obtain evidence that explanations increase users’ trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so that we can re-design a better, more human-centered explanation interface.

Extended Reality Applications for Space Health

INTRODUCTION: Spaceflight has detrimental effects on human health, imposing significant and unique risks to crewmembers due to physiological adaptations, exposure to physical and psychological stressors, and limited capabilities to provide medical care. Previous research has proposed and evaluated several strategies to support and mitigate the risks related to astronauts' health and medical exploration capabilities. Among these, extended reality (XR) technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR) have increasingly been adopted for training, real-time clinical, and operational support in both terrestrial and aerospace settings, and only a few studies have reported research results on the applications of XR technologies for improving space health. This study aims to systematically review the scientific literature that has explored the application of XR technologies in the space health field. We also discuss the methodological and design characteristics of the existing studies in this realm, informing future research and development efforts on applying XR technologies to improve space health and enhance crew safety and performance.Ebnali M, Paladugu P, Miccile C, Park SH, Burian B, Yule S, Dias RD. Extended reality applications for space health. Aerosp Med Hum Perform. 2023; 94(3):122-130.

Electronic health records in nursing from 2000 to 2020: A bibliometric analysis

Background

Electronic health records (EHR) is the longitudinal data generated by patients in medical institutions and recorded by electronic medical information systems in the form of digital, which is also the most widespread application of big data in medicine. The purpose of this study was to explore the application of electronic health records in the field of nursing and determine the current research status and hotspots.

Methods

A bibliometric analysis of electronic health records in nursing was undertaken from 2000 to 2020. The literature comes from Web of Science Core Collection database. We used CiteSpace (version 5.7 R5; Drexel University), which is a Java-based software that especially visualized collaborative networks and research topics.

Results

A total of 2616 publications were included in the study. We found that publications increased year by year. The Journal of American Medical Informatics Association (n = 921) is the most cited. The United States (n = 1,738) has the most publications in this field. University Penn (n = 63) is the institution with the most publications. There is no influential cooperation network among the authors, of which Bates, David W (n = 12) have the largest number of publications. The relevant publications also focus on the fields of health care science and services, and medical informatics. In keywords, EHR, long-term care, mobile application, inpatient falls, and advance care planning has been researching hotspots in recent years.

Conclusion

With the popularization of information systems, the publications of EHR in the nursing field have increased year by year. This study provides the basic structure, potential cooperation, and research trends of EHR in the field of nursing from 2000 to 2020, and provides a reference for nurses to effectively use EHR to help clinical work or scientific researchers explore the potential significances of EHR.

Care Disruption During COVID-19: a National Survey of Hospital Leaders

BACKGROUND

The COVID-19 pandemic caused massive disruption in usual care delivery patterns in hospitals across the USA, and highlighted long-standing inequities in health care delivery and outcomes. Its effect on hospital operations, and whether the magnitude of the effect differed for hospitals serving historically marginalized populations, is unknown.

OBJECTIVE

To investigate the perspectives of hospital leaders on the effects of COVID-19 on their facilities' operations and patient outcomes.

METHODS

A survey was administered via print and electronic means to hospital leaders at 588 randomly sampled acute-care hospitals participating in Medicare's Inpatient Prospective Payment System, fielded from November 2020 to June 2021. Summary statistics were tabulated, and responses were adjusted for sampling strategy and non-response.

RESULTS

There were 203 responses to the survey (41.6%), with 20.7% of respondents representing safety-net hospitals and 19.7% representing high-minority hospitals. Over three-quarters of hospitals reported COVID testing shortages, about two-thirds reported staffing shortages, and 78.8% repurposed hospital spaces to intensive care units, with a slightly higher proportion of high-minority hospitals reporting these effects. About half of respondents felt that non-COVID inpatients received worsened quality or outcomes during peak COVID surges, and almost two-thirds reported worsened quality or outcomes for outpatient non-COVID patients as well, with few differences by hospital safety-net or minority status. Over 80% of hospitals participated in alternative payment models prior to COVID, and a third of these reported decreasing these efforts due to the pandemic, with no differences between safety-net and high-minority hospitals.

CONCLUSIONS

COVID-19 significantly disrupted the operations of hospitals across the USA, with hospitals serving patients in poverty and racial and ethnic minorities reporting relatively similar care disruption as non-safety-net and lower-minority hospitals.

User Satisfaction of Using Electronic Medical Record System and Its Associated Factors among Healthcare Professionals in Ethiopia: A Cross-Sectional Study

Background

Electronic medical record (EMR) systems have become essential for the proper management of patients' information. Electronic medical record systems are on the rise in developing countries due to the need to ensure improved quality of healthcare. However, EMR systems can be ignored, if users are not satisfied with the implemented system. User dissatisfaction has been associated with the failure of EMR systems as a primary factor. There is also limited research done in the Ethiopian context on EMR user satisfaction at private hospitals. This study is aimed at assessing user satisfaction with electronic medical records and associated factors among health professionals working at private hospitals in Addis Ababa.

Methods

Institution-based cross-sectional quantitative study was conducted among health professionals working at private hospitals in Addis Ababa, from March to April 2021. A self-administered questionnaire was used to collect the data. EpiData version 4.6 and Stata version 25 were used for data entry and analysis, respectively. Descriptive analyses were computed for the study variables. Bivariable and multivariable logistic regression analyses were carried out to assess the significance of independent variables on dependent variables.

Results

A total of 403 (95.33% response rate) participants completed all the questionnaires. More than half of 214 (53.10%) of the participants were satisfied with the EMR system. Factors associated with user satisfaction with electronic medical records were good computer literacy (AOR = 2.92, 95% CI: [1.16-7.37]), perceived information quality (AOR = 3.54, 95% CI: [1.55-8.11]), perceived service quality (AOR = 3.15, 95% CI: [1.58-6.28]), perceived system quality (AOR = 3.05, 95% CI: [1.32-7.05]), EMR training (AOR = 4.00, 95% CI: [1.76-9.03]), computer access (AOR = 3.17, 95% CI: [1.19-8.46]), and HMIS training (AOR = 2.05, 95% CI: [1.22-6.71]).

Conclusions

In this study, health professionals' EMR satisfaction was moderate. The result showed that EMR training, computer literacy, computer access, perceived system quality, information quality, service quality, and HMIS training were associated with user satisfaction. Improving computer-related training, system quality, information quality, and service quality is an important intervention to improve the healthcare professional's satisfaction towards using electronic health record systems in Ethiopia.

An anonymization-based privacy-preserving data collection protocol for digital health data

Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients, which makes it challenging. To collect health data without privacy breaches, it must be secured between the data owner and the collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third-party involvement, which makes them less applicable for privacy-preserving healthcare data collection. This article proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model was adopted to efficiently prevent identity disclosure attacks, and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack, known as "leader collusion", in which the elected representatives may collaborate to violate an individual's privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted in real-world datasets, and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.

A survey of the knowledge, perceptions of and attitudes to digital health of healthcare professionals in 14 Bulgarian hospitals: First large-scale study on digital health in Bulgarian inpatient facilities

Objective

To explore the knowledge, perceptions of and attitudes to digital health of Bulgarian hospital professionals in the first study of digital health in this professional group.

Methods

A paper-based questionnaire was administered to doctors, trainee doctors, nurses, midwives, and laboratory assistants working in multiprofile or specialized hospitals. Topics included the following: state, objectives, benefits, and future of digital health; data storage, access, security, and sharing; main software used; patient-held Personal Information System (PIS); and telemedicine. A total of 1187 participants from 14 hospitals completed the survey in two phases: September 2013-April 2014 and May 2015-April 2017. Data were analyzed through descriptive statistics and multilevel logistic regression.

Results

Three-quarters of participants evaluated the state of development of digital health in Bulgaria as subpar (36.0% negative; 38.9% passable; 24.5% positive). 27.2% (323) endorsed patients having unconditional access to their data. In contrast, 89.5% (1062) of participants considered it appropriate to have full access to patient data recorded by colleagues. Doctors were more likely to endorse patients having access to their data than healthcare specialists (OR = 1.79 at facility, OR = 1.77 at location).

Conclusion

The largely negative or lukewarm attitudes toward the state of development of digital health in Bulgaria are likely to result from the high number of failed projects, unmet expectations, misunderstood benefits, and unforeseen challenges. This study provides a much-needed stimulus and baseline for researching the ways in which the digital health landscape in Bulgaria has matured-or not.

Digital literacy level and associated factors among health professionals in a referral and teaching hospital: An implication for future digital health systems implementation

Background

In Ethiopia and other developing countries, electronic medical record systems and other health information technology are being introduced. However, a small proportion of low-income countries have successfully implemented national health information systems. One cause for this can be the lack of digital literacy among medical practitioners. As a result, this study aimed to assess health professionals' digital literacy level and associated factors in Northwest Ethiopia.

Method

A quantitative cross-sectional study was employed among 423 health professionals working in a teaching and referral hospital in Northwest Ethiopia. We modified and applied the European commission's framework for digital competency to assess the level of digital literacy among health professionals. We used stratified random sampling with proportional allocation to the size of the departments in the hospital to select study participants. Data were collected using a semi-structured, self-administered, and pretested questionnaire. Descriptive and binary logistic regression analysis techniques were used to describe respondents' digital literacy level and identify its associated factor, respectively. The odds ratio with 95% CI and value of p were used to assess the strength of the association and statistical significance, respectively.

Results

Out of 411 participants, 51.8% (95% CI, 46.9-56.6%) of health professionals had adequate digital literacy. Holding a master's degree (Adjusted OR = 2.13, 95% CI: 1.18-3.85), access to digital technology (AOR = 1.89, 95% CI: 1.12-3.17), having training in digital technology (AOR = 1.65, 95% CI: 1.05-2.59), and having a positive attitude towards digital health technology (AOR = 1.64, 95% CI: 1.02-2.68) were found to be significant factors associated with health professionals digital literacy level of health professionals.

Conclusion

Low level of digital literacy among health professionals was observed, with nearly half (48.2%) of them having poor digital literacy levels. Access to digital technology, training on digital technology, and attitude toward digital health technology were significant factors associated with digital literacy. It is suggested to increase computer accessibility, provide a training program on digital health technology, and promote a positive attitude toward this technology to improve the deployment of health information systems.

Importance of Incorporating the Perspectives of People with Cancer into Oncology Education: A Scoping Review

Background

With the shift towards person-centered care (PCC) in oncology, there is a need for parallel evolution of oncology education programs to prepare the next generation of health professionals to deliver effective PCC. These programs should be designed utilizing perspectives from individuals who have lived experience with cancer to ensure that changes to education curricula translate to improved PCC in the clinic.

Objectives

Our goal was to identify existing literature describing such programs as well as identify gaps for further development.

Methods

Keywords were agreed upon and searched across Ovid Medline, Ovid Embase, ERIC, Google Scholar, and MedEdPORTAL databases. Duplicates were removed, unique articles were screened for relevance by title and abstract, and a full text review of each article was completed for validation. Included articles describe methods for involving people with cancer in developing and/or delivering oncology-focused education programs.

Results

In total, 15 articles describing 12 unique oncology education programs from 7 different countries were identified, reviewed, and summarized. These programs involved learners undertaking undergraduate medical education, postgraduate medical education, continuing medical education, or training as nurses or radiation therapists. Current literature indicates that classroom-based sessions, experiential or simulated learning modalities, and/or asynchronous online modules can all feasibly be created with the integration of perspectives/narratives of people with cancer. Furthermore, involving people with cancer directly in the design and/or delivery of these education programs may contribute to improved patient experiences.

Conclusions

Including the perspectives of people with cancer directly in oncology curriculum development and delivery can improve established pedagogical approaches and enhance learner confidence and competency in delivering PCC. We provide recommendations for stepwise implementation of patient perspectives into oncology education, with the hope that future programs will better prepare and motivate learners to provide PCC aimed at improving cancer care, quality of life, and disease outcomes.

Achieving optimal heath data impact in rural African healthcare settings: measures to barriers in Bukomansimbi District, Central Uganda

BACKGROUND

Health data is one of the most valuable assets in health service delivery yet one of the most underutilized in especially low-income countries. Health data is postulated to improve health service delivery through availing avenues for optimal patient management, facility management, and public health surveillance and management. Advancements in information technology (IT) will further increase the value of data, but will also call for capacity readiness especially in rural health facilities. We aimed to understand the current knowledge, attitudes and practices of health workers towards health data management and utilization.

METHODS

We conducted key informant interviews (KII) for health workers and data staff, and focus group discussions (FGD) for the village health teams (VHTs). We used both purposive and convenience sampling to recruit key informants, and convenience sampling to recruit village health teams. Interviews and discussions were audiotaped and transcribed verbatim. We manually generated the codes and we used thematic analysis to identify the themes. We also developed a reflexivity journal.

RESULTS

We conducted a total of 6 key informant interviews and 3 focus group discussions of 29 participants. Our analysis identified 7 themes: One theme underscored the health workers' enthusiasm towards an optimal health data management setting. The rest of the six themes resonated around working remedies to the systemic challenges that grapple health data management and utilization at facilities in rural areas. These include: Building human resource capacity; Equipping the facilities; Improved coordination with partners; Improved data quality assurance; Promotion of a pull supply system and Reducing information relay time.

CONCLUSION

Our findings reveal a plethora of systematic challenges that have persistently undercut optimal routine health data management and utilization in rural areas and suggest possible working remedies. Health care workers express enthusiasm towards an optimal health management system but this isn't matched by their technical capacity, facility readiness, systems and policy willingness. There is an urgent need to build rural lower facilities' capacity in health data management and utilization which will also lay a foundation for exploitation of information technology in health.

Exploring facilitators of the implementation of electronic health records in Saudi Arabia

INTRODUCTION

The introduction of information technology was one of the key priorities for policy-makers in health care organisations over the last two decades due to the potential benefits of this technology to improve health care services and quality. However, approximately 50% of those projects failed to achieve their intended aims. This was a result of several factors, including the cost of these projects. The Saudi Ministry of Health (MoH) planned to implement an electronic health record system (EHRS) in approximately 2100 primary health care centres nationwide. It was acknowledged that this project may face hurdles, which might result in the failure of the project if implementation facilitators were not first determined. According to the Saudi MoH, previous electronic health record system implementation in primary health care centres failed as a consequence of several barriers, such as poor infrastructure, lack of connectivity and lack of interoperability. However, the facilitators of successful electronic health record system implementation in Saudi primary health care centres are not understood.

AIM

To determine the facilitators that enhance the success of the implementation of an EHRS in public primary health care centres in SA.

METHOD

A mixed methods approach was used with both qualitative and quantitative methods (qualitative using semistructured interviews and quantitative with a closed survey). The purpose of the utilisation of exploratory mixed methods was to identify a wide range of facilitators that may influence EHRS implementation. The data were obtained from two different perspectives, primary health care centre practitioners and project team members. A total of 351 practitioners from 21 primary health care centres participated in the online survey, and 14 key informants at the Saudi Ministry of Health who were directly involved in the electronic health record system implementation in the primary health care centres agreed to be interviewed face to face.

RESULTS

The findings from both studies revealed several facilitators. Among these facilitators, financial resources were found to be the most influential factor that assisted in overcoming some barriers, such as software selection. The size of the primary health care centres was the second facilitator of successful implementation, despite the scale of the project. Perceived usefulness was another facilitator identified in both the interviews and the survey. More than 90% of the participants thought that the electronic health record system was useful and could contribute to improving the quality of health care services. While a high level of satisfaction was expressed towards the electronic health record system's usability and efficiency, low levels of satisfaction were recorded for organisational factors such as user involvement, training and support. Hence, system usability and efficiency were documented to be other facilitators of successful electronic health record system implementation in Saudi primary health care centres.

CONCLUSION

The findings of the present study suggest that sufficient financial support is essential to enhance the success of electronic health record system implementation despite the scale of the project. Additionally, effective leadership and project management are core factors to overcome many obstacles and ensure the success of large-scale projects.

A Retrospective Cohort Study Evaluating the Association Between Implementation of a Digital Care Plan and Hospitalization Rates for Home Care Residents in the United Kingdom

Cera, a homecare provider, uses digital care plans (DCP), to streamline the provision of home care. DCP rollout is part of a larger digitization initiative, including carer visit reports collected through a mobile app and branch actions recorded in a web application supported by a secure central database. This retrospective cohort study aimed to assess the association of a DCP rollout with service user hospitalization rates. his study utilized retrospective data from 2 groups of service users, those for whom their first 30 days of Cera membership occurred prior to DCP rollout (pre-DCP group) versus those whose first 30 days of Cera membership occurred after DCP rollout (post-DCP group). The 30-day hospitalization rate was the primary outcome measure and was determined through a combination of carer reports, reporting from service users or their families, and branch staff follow-up. There were 55 hospitalizations among 392 users in the pre-DCP group in the 30 days after joining Cera (14.0% hospitalization rate), compared to 23 hospitalizations among 297 users in the post-DCP group (7.7% hospitalization rate). This represented a significant reduction in hospitalizations in the post-DCP group (6.3% absolute difference in hospitalization rate; 45% relative reduction; P < .001). This result was robust to multiple sensitivity analyses. The implementation of a DCP was associated with a 45% relative reduction in the 30-day hospitalization rate for new service users when compared to pre-DCP enrollment. These benefits could be further amplified by combining the DCP with additional initiatives aimed at the prediction and prevention of avoidable hospitalizations.

The Use of Electronic Health Record Metadata to Identify Nurse-Patient Assignments in the Intensive Care Unit: Algorithm Development and Validation

Background

Nursing care is a critical determinant of patient outcomes in the intensive care unit (ICU). Most studies of nursing care have focused on nursing characteristics aggregated across the ICU (eg, unit-wide nurse-to-patient ratios, education, and working environment). In contrast, relatively little work has focused on the influence of individual nurses and their characteristics on patient outcomes. Such research could provide granular information needed to create evidence-based nurse assignments, where a nurse’s unique skills are matched to each patient’s needs. To date, research in this area is hindered by an inability to link individual nurses to specific patients retrospectively and at scale.

Objective

This study aimed to determine the feasibility of using nurse metadata from the electronic health record (EHR) to retrospectively determine nurse-patient assignments in the ICU.

Methods

We used EHR data from 38 ICUs in 18 hospitals from 2018 to 2020. We abstracted data on the time and frequency of nurse charting of clinical assessments and medication administration; we then used those data to iteratively develop a deterministic algorithm to identify a single ICU nurse for each patient shift. We examined the accuracy and precision of the algorithm by performing manual chart review on a randomly selected subset of patient shifts.

Results

The analytic data set contained 5,479,034 unique nurse-patient charting times; 748,771 patient shifts; 87,466 hospitalizations; 70,002 patients; and 8,134 individual nurses. The final algorithm identified a single nurse for 97.3% (728,533/748,771) of patient shifts. In the remaining 2.7% (20,238/748,771) of patient shifts, the algorithm either identified multiple nurses (4,755/748,771, 0.6%), no nurse (14,689/748,771, 2%), or the same nurse as the prior shift (794/748,771, 0.1%). In 200 patient shifts selected for chart review, the algorithm had a 93% accuracy (ie, correctly identifying the primary nurse or correctly identifying that there was no primary nurse) and a 94.4% precision (ie, correctly identifying the primary nurse when a primary nurse was identified). Misclassification was most frequently due to patient transitions in care location, such as ICU transfers, discharges, and admissions.

Conclusions

Metadata from the EHR can accurately identify individual nurse-patient assignments in the ICU. This information enables novel studies of ICU nurse staffing at the individual nurse-patient level, which may provide further insights into how nurse staffing can be leveraged to improve patient outcomes.

Digital Disruption and Big Data in Healthcare - Opportunities and Challenges

Background

As the amount of medical data in the electronic medical records system (EMR) is increasing tremendously, the required time to read it by health providers is growing by the exact proportionality. This means that physicians must increase the time spared for each patient again by the precise proportionality. This may lead to exposing the accuracy and quality of the course of action to be taken for the patients. Increasing the physician’s required time for one patient means that the physician can see fewer patients. This will create an issue with the medical management authority as more physicians are needed, and higher expenses will be required.

Purpose

The two questions that arise here are 1. Identify the potential opportunities and challenges for extensive data analysis in the healthcare sector. 2. Evaluate different ways in which big medical data can be analyzed?

Methods

The authors identified the four concerned parties representing the four potential solutions dimensions to answer these two questions. These parties are 1. physicians, 2. health information systems management (HISM) departments, mainly the EMR system, and 3. Health management departments 4. Relevant Health Information Systems (HIS) parties. A literature review and 25 interviews were conducted. The interviews covered 1: Two global organizations: John Hopkins and Joint Commission International (JCI), 2: Three United Arab Emirates-based health organizations: Department of health in Abu Dhabi, SEHA in Abu Dhabi, Dubai health Authority (DHA) in Dubai, 3: 10 Physicians from different specialties, 4: Five EMR managers and 5: Five IT (Information Technology) professionals representing the HIS parties. Qualitative analysis is used as the approach for data analysis.

Results

Identifying the managerial and the technical recommendations to be utilized mainly based on digital disruption technologies, tools, and processes.

Conclusion

Healthcare has been slow in embracing digital disruption and transformation. In most areas, it is still in the initial stages. Recommendations are based on the UAE cases, highlighting the specific technologies and their features.

A scoping review of complication prediction models in spinal surgery: An analysis of model development, validation and impact

Background

Predictive analytics are being used increasingly in the field of spinal surgery with the development of models to predict post-surgical complications. Predictive models should be valid, generalizable, and clinically useful. The purpose of this review was to identify existing post-surgical complication prediction models for spinal surgery and to determine if these models are being adequately investigated with internal/external validation, model updating and model impact studies.

Methods

This was a scoping review of studies pertaining to models for the prediction of post-surgical complication after spinal surgery published over 10 years (2010-2020). Qualitative data was extracted from the studies to include study classification, adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and risk of bias (ROB) assessment using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). Model evaluation was determined using area under the curve (AUC) when available. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was used as a basis for the search methodology in four different databases.

Results

Thirty studies were included in the scoping review and 80% (24/30) included model development with or without internal validation. Twenty percent (6/30) were exclusively external validation studies and only one study included an impact analysis in addition to model development and internal validation. Two studies referenced the TRIPOD guidelines and there was a high ROB in 100% of the studies using the PROBAST tool.

Conclusions

The majority of post-surgical complication prediction models in spinal surgery have not undergone standardized model development and internal validation or adequate external validation and impact evaluation. As such there is uncertainty as to their validity, generalizability, and clinical utility. Future efforts should be made to use existing tools to ensure standardization in development and rigorous evaluation of prediction models in spinal surgery.

Hospital's adoption of multiple methods of obtaining outside information and use of that information

OBJECTIVE

Hospitals have multiple methods available to engage in health information exchange (HIE); however, it is not well understood whether these methods are complements or substitutes. We sought to characterize patterns of adoption of HIE methods and examine the association between these methods and increased availability and use of patient information.

MATERIALS AND METHODS

Cross-sectional analysis of 3208 nonfederal acute care hospitals in the 2019 American Hospital Association Information Technology Supplement.

RESULTS

The median hospital obtained outside information through 4 methods. Hospitals that obtained data through a regional HIE organization were 2.2 times more likely to also obtain data via Direct using a health information service provider (HISP) than hospitals that did not (P < .001). Hospitals in a single electronic health record (EHR) vendor network were no more or less likely to participate in a HISP or HIE. Six of 7 methods were associated with greater information availability. Only 4 of 7 methods (portals, interfaces, single vendor networks and multi-vendor networks but not access to outside EHR, regional exchange or Direct using a HISP) were associated with more frequent use of information, and single vendor networks were most strongly associated with more frequent use (odds ratio = 4.7, P < .001).

DISCUSSION

Adoption of some methods was correlated, indicating complementary use. Few methods were negatively correlated, indicating limited competition. Although information availability was common, low correlation with use indicated that challenges related to integration may be slowing use of information.

CONCLUSION

Complementarities between methods, and the role of integration in supporting information use, indicate the potential value of efforts aimed at ensuring exchange methods work well together, such as the Trusted Exchange Framework and Common Agreement.

Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals

Background

Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals.

Objective

This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals.

Methods

This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time.

Results

From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system—1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53).

Conclusions

As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.

AutoScore-Imbalance: An Interpretable Machine Learning Tool for Development of Clinical Scores with Rare Events Data

BACKGROUND

Medical decision-making impacts both individual and public health. Clinical scores are commonly used among various decision-making models to determine the degree of disease deterioration at the bedside. AutoScore was proposed as a useful clinical score generator based on machine learning and a generalized linear model. However, its current framework still leaves room for improvement when addressing unbalanced data of rare events.

METHODS

Using machine intelligence approaches, we developed AutoScore-Imbalance, which comprises three components: training dataset optimization, sample weight optimization, and adjusted AutoScore. Baseline techniques for performance comparison included the original AutoScore, full logistic regression, stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), full random forest, and random forest with a reduced number of variables. These models were evaluated based on their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i.e., mean value of sensitivity and specificity). By utilizing a publicly accessible dataset from Beth Israel Deaconess Medical Center, we assessed the proposed model and baseline approaches to predict inpatient mortality.

RESULTS

AutoScore-Imbalance outperformed baselines in terms of AUC and balanced accuracy. The nine-variable AutoScore-Imbalance sub-model achieved the highest AUC of 0.786 (0.732-0.839), while the eleven-variable original AutoScore obtained an AUC of 0.723 (0.663-0.783), and the logistic regression with 21 variables obtained an AUC of 0.743 (0.685-0.800). The AutoScore-Imbalance sub-model (using a down-sampling algorithm) yielded an AUC of 0.771 (0.718-0.823) with only five variables, demonstrating a good balance between performance and variable sparsity. Furthermore, AutoScore-Imbalance obtained the highest balanced accuracy of 0.757 (0.702-0.805), compared to 0.698 (0.643-0.753) by the original AutoScore and the maximum of 0.720 (0.664-0.769) by other baseline models.

CONCLUSIONS

We have developed an interpretable tool to handle clinical data imbalance, presented its structure, and demonstrated its superiority over baselines. The AutoScore-Imbalance tool can be applied to highly unbalanced datasets to gain further insight into rare medical events and facilitate real-world clinical decision-making.

Electronic Health Records and Heart Failure

Increasing the global adoption of electronic health records (EHRs) is transforming the delivery of clinical care. EHRs offer tools that are useful in the care of heart failure ranging from individualized risk stratification and decision support to population management. EHR tools can be combined to target specific areas of need such as the standardization of care, improved quality of care, and resource management. Leveraging EHR functionality has been shown to improve select outcomes including guideline-based therapies, reduction in adverse clinical outcomes, and improved cost-efficiency. Central to success is participation by clinicians and patients in the design and feedback of EHR tools.

Roadmap to a more useful and usable electronic health record

Background

A decade after the Health Information Technology for Economic and Clinical Health (HITECH) Act, electronic health records (EHRs) largely remain poorly designed and contribute to clinician burnout.

Objective

The purpose of this study was to understand clinicians’ wants, needs, and perceived barriers imposed by the EHR; implement best practices in user-centered design; and create a clinician-centered EHR framework validated via a functional EHR prototype.

Methods

Usability evaluations were performed using a simulated patient with a complex clinical scenario. Convergent parallel mixed methods linked to action research and agile development were used to create an EHR prototype based on clinician-centered design. Prototype functionality was validated via a final usability evaluation.

Results

Between 2015 and 2017, 53 clinicians from 8 cardiology practices (4 academic and 4 private) participated in initial evaluations of their installed EHR. In 2019, 25 clinicians participated in final evaluations of their EHR vs our EHR prototype. Initial evaluations documented that clinicians judged the EHRs as poorly designed, scoring a mean of 47.1 on the System Usability Scale. Clinicians expressed that EHRs impeded workflow and communication and prolonged their workday. In the final evaluations, no improvement in installed EHRs was found (mean score 48.1); however, the EHR prototype was assessed as significantly more usable (mean score 77.8; P <.001).

Conclusion

A decade after the HITECH Act, EHRs still receive low usability scores. By applying user-centered design, an EHR prototype with improved features, functionality, and workflow integration was developed. Clinician testing of the EHR prototype demonstrated it was significantly more useful and usable to clinicians, thus identifying a framework and pathway for substantive improvement of EHR systems.

Association of Hospital Interoperable Data Sharing With Alternative Payment Model Participation

Importance

Interoperable patient data exchange across hospitals remains an important policy goal for reducing costs and improving the quality of care. Congress designated 2018 as the goal for nationwide interoperability, and policy makers hoped that aligning financial incentives via alternative payment models (APMs) would help achieve that goal.

Objective

To measure interoperability progress since 2014, assess the association between alternative payment model participation and hospital engagement in interoperable data sharing from 2014 to 2018, and evaluate hospital-reported barriers to interoperability in 2018.

Design Setting and Participants

This cohort study included nonfederal acute care hospitals in the US from January 2014 to December 2018 that responded to the American Hospital Association Annual Survey. Data were analyzed from October 2019 through March 2021.

Exposures

Participation in an APM, including accountable care organizations, bundled payments, or patient-centered medical homes.

Main Outcomes and Measures

Hospital engagement in all 4 domains of interoperability: finding/querying for data, sending data electronically, receiving data electronically, and integrating electronic patient data from external care delivery organizations.

Results

The sample included 3928 hospitals in the US from January 2014 to December 2018. Progress across interoperability domains was uneven, 2430 (88.3%) hospitals sending and 2115 (76.9%) receiving patient data electronically in 2018. However, only 1249 (45.4%) hospitals engaged in all 4 domains of interoperability in 2018, and growth between 2014 and 2018 was slow. There was no evidence that participation in APMs was associated with interoperability, with multivariate models suggesting that participation in an APM was associated with only a non-statistically significant 1-percentage point increase in interoperability engagement (β = 0.01; 95% CI, -0.01 to 0.03). The most commonly cited barrier to interoperability was challenges associated with sharing data across different electronic health record vendors.

Conclusions and Relevance

In this cohort study of hospital interoperability, fewer than half of US hospitals were engaged in interoperable data exchange in 2018. There was no observable evidence that hospital APM participation was associated with interoperability engagement. Many hospitals report technical and governance challenges to data sharing that are unlikely to be addressed by the alignment of financial incentives alone.

Mortality among US veterans after emergency visits to Veterans Affairs and other hospitals: retrospective cohort study

OBJECTIVE

To measure and compare mortality outcomes between dually eligible veterans transported by ambulance to a Veterans Affairs hospital and those transported to a non-Veterans Affairs hospital.

DESIGN

Retrospective cohort study using data from medical charts and administrative files.

SETTING

Emergency visits by ambulance to 140 Veteran Affairs and 2622 non-Veteran Affairs hospitals across 46 US states and the District of Columbia in 2001-18.

PARTICIPANTS

National cohort of 583 248 veterans (aged ≥65 years) enrolled in both the Veterans Health Administration and Medicare programs, who resided within 20 miles of at least one Veterans Affairs hospital and at least one non-Veterans Affairs hospital, in areas where ambulances regularly transported patients to both types of hospitals.

INTERVENTION

Emergency treatment at a Veterans Affairs hospital.

MAIN OUTCOME MEASURE

Deaths in the 30 day period after the ambulance ride. Linear probability models of mortality were used, with adjustment for patients' demographic characteristics, residential zip codes, comorbid conditions, and other variables.

RESULTS

Of 1 470 157 ambulance rides, 231 611 (15.8%) went to Veterans Affairs hospitals and 1 238 546 (84.2%) went to non-Veterans Affairs hospitals. The adjusted mortality rate at 30 days was 20.1% lower among patients taken to Veterans Affairs hospitals than among patients taken to non-Veterans Affairs hospitals (9.32 deaths per 100 patients (95% confidence interval 9.15 to 9.50) v 11.67 (11.58 to 11.76)). The mortality advantage associated with Veterans Affairs hospitals was particularly large for patients who were black (-25.8%), were Hispanic (-22.7%), and had received care at the same hospital in the previous year.

CONCLUSIONS

These findings indicate that within a month of being treated with emergency care at Veterans Affairs hospitals, dually eligible veterans had substantially lower risk of death than those treated at non-Veterans Affairs hospitals. The nature of this mortality advantage warrants further investigation, as does its generalizability to other types of patients and care. Nonetheless, the finding is relevant to assessments of the merit of policies that encourage private healthcare alternatives for veterans.

Barriers and Benefits of Information Communication Technologies Used by Health Care Aides

BACKGROUND

Although information and communication technologies (ICT) are becoming more common among health care providers, there is little evidence on how ICT can support health care aides. Health care aides, also known as personal care workers, are unlicensed service providers who encompass the second largest workforce, next to nurses, that provide care to older adults in Canada.

OBJECTIVE

The purpose of this literature review is to examine the range and extent of barriers and benefits of ICT used by health care workers to manage and coordinate the care-delivery workflow for their clients.

METHODS

We conducted a literature review to examine the range and extent of ICT used by health care aides to manage and coordinate their care delivery, workflow, and activities. We identified 8,958 studies of which 40 were included for descriptive analyses.

RESULTS

We distinguished the following five different purposes for the use and implementation of ICT by health care aides: (1) improve everyday work, (2) access electronic health records for home care, (3) facilitate client assessment and care planning, (4) enhance communication, and (5) provide care remotely. We identified 128 barriers and 130 benefits related to adopting ICT. Most of the barriers referred to incomplete hardware and software features, time-consuming ICT adoption, heavy or increased workloads, perceived lack of usefulness of ICT, cost or budget restrictions, security and privacy concerns, and lack of integration with technologies. The benefits for health care aides' adoption of ICT were improvements in communication, support to workflows and processes, improvements in resource planning and health care aides' services, and improvements in access to information and documentation.

CONCLUSION

Health care aides are an essential part of the health care system. They provide one-on-one care to their clients in everyday tasks. Despite the scarce information related to health care aides, we identified many benefits of ICT adoption.