Electronic Health Record Patient Research Articles

Long-Term Outcomes Associated With β-Lactam Allergies

β-lactam (BL) allergies are the most common drug allergy worldwide, but most are reported in error. BL allergies are also well-established risk factors for adverse drug events and antibiotic-resistant infections during inpatient health care encounters, but the understanding of the long-term outcomes of patients with BL allergies remains limited. To evaluate the long-term clinical outcomes of patients with BL allergies. This longitudinal retrospective cohort study was conducted at a single regional health care system in western Pennsylvania. Electronic health records were analyzed for patients who had an index encounter with a diagnosis of sepsis, pneumonia, or urinary tract infection between 2007 and 2008. Patients were followed-up until death or the end of 2018. Data analysis was performed from January 2022 to January 2024. The presence of any BL class antibiotic in the allergy section of a patient's electronic health record, evaluated at the earliest occurring observed health care encounter. The primary outcome was all-cause mortality, derived from the Social Security Death Index. Secondary outcomes were defined using laboratory and microbiology results and included infection with methicillin-resistant Staphylococcus aureus (MRSA), Clostridium difficile, or vancomycin-resistant Enterococcus (VRE) and severity and occurrence of acute kidney injury (AKI). Generalized estimating equations with a patient-level panel variable and time exposure offset were used to evaluate the odds of occurrence of each outcome between allergy groups. A total of 20 092 patients (mean [SD] age, 62.9 [19.7] years; 12 231 female [60.9%]), of whom 4211 (21.0%) had BL documented allergy and 15 881 (79.0%) did not, met the inclusion criteria. A total of 3513 patients (17.5%) were Black, 15 358 (76.4%) were White, and 1221 (6.0%) were another race. Using generalized estimating equations, documented BL allergies were not significantly associated with the odds of mortality (odds ratio [OR], 1.02; 95% CI, 0.96-1.09). BL allergies were associated with increased odds of MRSA infection (OR, 1.44; 95% CI, 1.36-1.53), VRE infection (OR, 1.18; 95% CI, 1.05-1.32), and the pooled rate of the 3 evaluated antibiotic-resistant infections (OR, 1.33; 95% CI, 1.30-1.36) but were not associated with C difficile infection (OR, 1.04; 95% CI, 0.94-1.16), stage 2 and 3 AKI (OR, 1.02; 95% CI, 0.96-1.10), or stage 3 AKI (OR, 1.06; 95% CI, 0.98-1.14). Documented BL allergies were not associated with the long-term odds of mortality but were associated with antibiotic-resistant infections. Health systems should emphasize accurate allergy documentation and reduce unnecessary BL avoidance.

E055 The experience of pain in patients with rheumatoid arthritis - findings from a qualitative study of REMORA app users’ experiences

Abstract Background/Aims The Remote Monitoring in Rheumatoid Arthritis (REMORA) system enables people with rheumatoid arthritis (RA) to track daily symptoms, such as pain and fatigue, via a smartphone app. Graphed symptoms are made available in the electronic health record for patients and clinicians to review together during clinical consultations. REMORA aims to help patients and clinicians to understand fluctuations in disease activity and flares in between clinical appointments. Here, we discuss how patients and clinicians interacted with the data in REMORA, and if and how these interactions provided new knowledge to support person-centred care. Methods Patients and clinicians recruited to take part in a REMORA feasibility trial were invited to participate in this qualitative study about their experience of using the system. We conducted semi-structured interviews (in person, or via telephone or video conference) and observed clinical consultations while taking field notes. Both interviews and clinic observations were audio-recorded, transcribed, and uploaded to NVivo 12 Plus software. Thematic analysis was guided by the Theoretical Domains Framework. Results 21 patients (of 32 consented patients who initiated symptom tracking) and seven clinicians from two study sites were interviewed. Five consultations were observed. Patients tracking their symptoms using the REMORA app highlighted how this helped them to remember periods where they had experienced pain. Clinicians were able to see where pain flares occurred. This created an opportunity for patients and clinicians to discuss pain, in turn supporting decision making around identifying and managing flares, and identifying positive responses to treatment. Some patients expressed difficulty in knowing whether to score all the joint pain they experienced, or just pain secondary to RA. At times they found it difficult to distinguish the origin of pain which created anxiety around score accuracy. For some patients, there was a dissonance between their pain experiences and the pain scores they reported in the REMORA app. For example, sometimes they entered scores that were lower than their actual pain levels because they did not want to admit, and formalise, how bad the pain was. Reviewing these pain scores together enabled clinicians and patients to discuss and interpret the symptom scores, giving clinicians new insights into patients’ approach to their RA. Conclusion The REMORA system captures longitudinal symptom scores to help direct clinical management plans. Patients engaged with the pain scoring function in various ways, and pain reports submitted through the app may not always fully represent people’s actual experiences of pain from their RA and other conditions. During consultations, skilful questioning by clinicians about patient-generated data can create new insights into how an individual copes with and manages their pain and wider long-term condition. Disclosure D. Dowding: None. S. Skyrme: None. Y. Masood: None. S.N. van der Veer: None. D.J. Griffiths-Jones: None. W. Dixon: Consultancies; WGD has received consultancy fees from Google unrelated to this work. C.A. Sharp: None.

Generative artificial intelligence responses to patient messages in the electronic health record: early lessons learned.

Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed. A retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses. Some LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations. Further work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.

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

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. 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. 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. 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.

420 Computable Phenotyping with “Big Data” as a Foundation for Artificial Intelligence Algorithm Construction: Puberty as a Transdisciplinary Case Example

OBJECTIVES/GOALS: Artificial intelligence (AI) depends on quality machine learning (ML) algorithms constructed with high-quality training data. This TL1 trainee project develops a disease-agnostic computable phenotype framework for ML algorithm construction, modeling male puberty as a case example. METHODS/STUDY POPULATION: A computable phenotype of male puberty was constructed to answer the question: “Does early pubertal timing increase the risk of developing type II diabetes (T2D) in males?” A computable phenotype of males < 18 years old was created in the TriNetX© Diamond Network utilizing Boolean operator data queries. TriNetX© contains patient electronic health record information (ICD-10 diagnoses, anthropometric measures). An exploratory analysis of patient counts reflecting various computable phenotypes allowed for outcome (T2D) comparison of males diagnosed with precocious puberty (E30.1, ICD code for early pubertal timing) to those without, controlling for body mass index (BMI). RESULTS/ANTICIPATED RESULTS: Subjects (n=12,996,132) displayed the following computable phenotype: Male, < 18 years old, without ever having a BMI documented >85th percentile. Males diagnosed with precocious puberty (E30.1) were 6.89 times more likely to develop T2D when aged 14-18 years old than those without (OR 6.89, 95% CI: 5.17-9.19, p<0.0001). Next steps involve training a ML model on each computable phenotype groupings’ health data, with anticipated results identifying underlying salient pathophysiologic variables. A generalized computable phenotype approach is further developed to: 1) explore clinical questions in large databases like TriNetX©, and 2) model disease development with AI/ML algorithm construction. DISCUSSION/SIGNIFICANCE: Computed phenotypes reveal males with precocious puberty may have increased T2D risk. Next steps utilize subject data to train an AI/ML algorithm, model development to identify salient pathophysiologic variables, and synthesize a generalized AI/ML developmental research framework for dissemination.

Implementing a pharmacist-integrated collaborative model of medication treatment for opioid use disorder in primary care: study design and methodological considerations

BackgroundPharmacists remain an underutilized resource in the treatment of opioid use disorder (OUD). Although studies have engaged pharmacists in dispensing medications for OUD (MOUD), few studies have evaluated collaborative care models in which pharmacists are an active, integrated part of a primary care team offering OUD care.MethodsThis study seeks to implement a pharmacist integrated MOUD clinical model (called PrIMO) and evaluate its feasibility, acceptability, and impact across four diverse primary care sites. The Consolidated Framework for Implementation Research is used as an organizing framework for study development and interpretation of findings. Implementation Facilitation is used to support PrIMO adoption. We assess the primary outcome, the feasibility of implementing PrIMO, using the Stages of Implementation Completion (SIC). We evaluate the acceptability and impact of the PrIMO model at the sites using mixed-methods and combine survey and interview data from providers, pharmacists, pharmacy technicians, administrators, and patients receiving MOUD at the primary care sites with patient electronic health record data. We hypothesize that it is feasible to launch delivery of the PrIMO model (reach SIC Stage 6), and that it is acceptable, will positively impact patient outcomes 1 year post model launch (e.g., increased MOUD treatment retention, medication regimen adherence, service utilization for co-morbid conditions, and decreased substance use), and will increase each site’s capacity to care for patients with MOUD (e.g., increased number of patients, number of prescribers, and rate of patients per prescriber).DiscussionThis study will provide data on a pharmacist-integrated collaborative model of care for the treatment of OUD that may be feasible, acceptable to both site staff and patients and may favorably impact patients’ access to MOUD and treatment outcomes.Trial registration: The study was registered on Clinicaltrials.gov (NCT05310786) on April 5, 2022, https://www.clinicaltrials.gov/study/NCT05310786?id=NCT05310786&rank=1

Exploring the Feasibility of Applying Deep Learning for the Early Prediction of Arthritis

Introduction: Arthritis is one of the most common chronic diseases. Early detection of arthritis and its progression can facilitate early intervention measures, lowering disease severity in patients. As electronic health records (EHR) become more accessible, this study assesses whether general health information and arthritis-related questionnaires can be used in arthritis diagnosis, without the involvement of costly imaging methods. Therefore, we created deep learning (DL) and machine learning (ML) models to explore the feasibility of combining EHR and modern computational tools to diagnose arthritis. Methods: A total of 782 arthritis patients and 4014 control patients were identified from the Osteoarthritis Initiative (OAI) – a ten-year-long observational study that included patient EHR in five time points. Six hundred variables were filtered by random forest classifier followed by manual filtering. Data were split properly to training, testing and validation set, and the training set was balanced. Sequential, nonsequential DL models, and five independent DL models for each time points were used. The accuracy, positive prevalence value (PPV), negative prevalence value (NPV), and area under curve (AUC), were assessed and compared with four classical ML models. SHAP (SHapley Additive exPlanations) summary analysis was also conducted. Results: Sequential and non-sequential deep learning models showed accuracies of ~ 0.97, and the four classical machine learning approaches showed accuracies of above 0.9. High positive and negative predicted values (> 0.90) for all of the models suggested the potential clinical applicability of the model, while the SHAP analysis demonstrated its interpretability. Discussion: We tested various models and showed the ability to use machine learning methods for early diagnosis of arthritis with EHR. The models can be used as a screening tool to select susceptible patients for confirmatory tests such as X-ray and MRI. Identification of early disease states could facilitate protective measures that slow disease progression.

Chronic liver disease-associated severe thrombocytopenia in Spain: Results from a retrospective study using machine learning and natural language processing

BackgroundPatients with chronic liver disease (CLD) often develop thrombocytopenia (TCP) as a complication. Severe TCP (platelet count<50×109/L) can increase morbidity and complicate CLD management, increasing bleeding risk during invasive procedures. ObjectivesTo describe the real-world scenario of CLD-associated severe TCP patients’ clinical characteristics. To evaluate the association between invasive procedures, prophylactic treatments, and bleeding events in this group of patients. To describe their need of medical resource use in Spain. MethodsThis is a retrospective, multicenter study including patients who had confirmed diagnosis of CLD and severe TCP in four hospitals within the Spanish National Healthcare Network from January 2014 to December 2018. We analyzed the free-text information from Electronic Health Records (EHRs) of patients using Natural Language Processing (NLP), machine learning techniques, and SNOMED-CT terminology. Demographics, comorbidities, analytical parameters and characteristics of CLD were extracted at baseline and need for invasive procedures, prophylactic treatments, bleeding events and medical resources used in the follow up period. Frequency tables were generated for categorical variables, whereas continuous variables were described in summary tables as mean (SD) and median (Q1–Q3). ResultsOut of 1,765,675 patients, 1787 had CLD and severe TCP; 65.2% were male with a mean age of 54.7 years old. Cirrhosis was detected in 46% (n=820) of patients and 9.1% (n=163) had hepatocellular carcinoma. Invasive procedures were needed in 85.6% of patients during the follow up period. Patients undergoing procedures compared to those patients without invasive procedures presented higher rates of bleeding events (33% vs 8%, p<0.0001) and higher number of bleedings. While prophylactic platelet transfusions were given to 25.6% of patients undergoing procedures, TPO receptor agonist use was only detected in 3.1% of them. Most patients (60.9%) required at least one hospital admission during the follow up and 14.4% of admissions were due to bleeding events with a hospital length of stay of 6 (3, 9) days. ConclusionsNLP and machine learning are useful tools to describe real-world data in patients with CLD and severe TCP in Spain. Bleeding events are frequent in those patients who need invasive procedures, even receiving platelet transfusions as a prophylactic treatment, increasing the further use of medical resources. Because that, new prophylactic treatments that are not yet generalized, are needed.

Integration of Substance Use Screening Into the Electronic Health Record for Adolescent Trauma Patients: A Quality Improvement Project.

Adolescent substance abuse is a well-acknowledged and increasing concern. Screening brief intervention and referral to treatment for alcohol and drug use in adolescent trauma is a requirement, but program implementation remains a challenge for many trauma centers. This study aims to examine the effect of an integrated electronic health record screening tool and staff training on screening, brief intervention, and referral to treatment compliance in adolescent trauma. This is a single-center, pre- and postintervention study of substance use screening compliance in adolescent trauma patients (age 12-21) conducted at a Level I pediatric trauma center in the Southeastern United States following the integration of the CRAFFT substance abuse screening tool into the electronic health record. The study compared 12months of preintervention data (January 2021 through January 2022) to 15months of postintervention data (February 2022 through May 2023). A total of N =241 patients met inclusion criteria, of which most were male, n =168 (69.7%), White n =185 (76.8%), and Hispanic n =179 (74.3%). Screening compliance increased from preintervention 81% to postintervention 92%. Our study demonstrates that integrating a digital screening tool into the electronic health record resulted in an average increased screening compliance of 11%.

Second- and third-year medical students' clinical encounters in the emergency department.

Experiential learning theory suggests that direct clinical experiences facilitate learning. Previous literature has focused primarily on the experiences of fourth-year medical students. As more students gain early clinical exposure, it is important to understand the types of patients seen by junior students. This study aims to categorize the clinical experiences of early (M2 and M3) students in the emergency department (ED). A retrospective review of the electronic health record of patients seen by M2s and M3s on a 2-week emergency medicine rotation at a single urban academic ED in the Midwest was performed. Data elements extracted included total number of patients seen, Emergency Severity Index (ESI), disposition, and chief complaint. Students were not mandated to see any particular patients. Medical students (248) saw 2994 total patients from 2018 to 2022. The median number of patients seen by each student was 12.0 (range 1-32). Pediatric patients made up 6.5% (n = 194) of total patients. Encounters were primarily ESI 2 or 3, which accounted for 89.4% of all patients (n = 2676). The most encountered complaints were abdominal pain, chest pain, and dyspnea, making up 15.6% (n = 467), 8.7% (n = 260), and 5.5% (n = 165), respectively, of total cases. Obstetrics/gynecology, hematologic, and environmental disorders were the least frequently encountered domains. No students saw all Clerkship Directors in Emergency Medicine (CDEM)-recommended complaints. There is significant variability in the ED encounters of M2s and M3s, with wide ranges of patient volume and presentations. This study provides some evidence that early students may not be meeting CDEM recommendations.

Real-Time Reporting of Complications in Hospitalized Surgical Patients by Surgical Team Members Using a Smartphone Application

The surgical morbidity and mortality (M&M) conference is a vital part of a resident's surgical education, but methods to collect and store M&M data are often rudimentary and unreliable. The authors propose a Health Insurance Portability and Accountability Act (HIPAA)-compliant, electronic health record (EHR)-connected application and database to report and store complication data. The app is linked to the patient's EHR, and as a result, basic data on each surgical case-including diagnosis, surgery type, and surgeon-are automatically uploaded to the app. In addition, all data are stored in a secure SQL database-with communications between the app and the database end-to-end encrypted for HIPAA compliance. The full surgical team has access to the app, democratizing complications reporting and allowing for reporting in both the inpatient and outpatient settings. This complication information can then be automatically pulled from the app with a premade presentation for the M&M conference. The data can also be accessed by a Power BI dashboard, allowing for easy quality improvement analyses. When implemented, the app improved data collection for the M&M conference while providing a database for institutional quality improvement use. The authors also identified additional utility of the app, including ensuring appropriate revenue capture. The general appearance of the app and the dashboard can be found in the article. The app developed in this project significantly improves on more common methods for M&M conference complication reporting-transforming M&M data into a valuable resource for resident education and quality improvement.

The Effect of Teach-Back on Readmission Rates in Rehabilitation Patients.

Thirty-day readmissions often occur in rehabilitation patients and can happen for many reasons. One of those reasons is when patients do not fully understand how to effectively manage their health after discharge. The purpose of this evidence-based quality improvement project was to determine if implementing the teach-back intervention from the Agency for Healthcare Research and Quality's (AHRQ) Health Literacy Universal Precautions Toolkit would impact 30-day readmission rates among adult rehabilitation patients. Data were collected from the electronic health record of rehabilitation patients. The comparative group included all rehabilitation admissions for 8 weeks prior to the intervention. The implementation group was composed of the rehabilitation admissions for 8 weeks post-implementation. All patients were then followed for 30 days postdischarge to capture readmissions. The total sample size was 79 ( n = 43 in the comparative group, n = 36 in the implementation group). There was a 45% decrease in the mean percentage of the 30-day readmission rate in the implementation group as compared with the comparative group. Based on the results, using the teach-back intervention from AHRQ's Health Literacy Universal Precautions Toolkit may impact 30-day readmission rates.

De-implementation of Fecal Occult Blood Testing in the Emergency Department and Hospital Units: A Quality Improvement Project.

Fecal occult blood tests (FOBT) are inappropriately used in patients with melena, hematochezia, coffee ground emesis, iron deficiency anemia, and diarrhea. The use of FOBT for reasons other than screening for colorectal cancer is considered low-value and unnecessary. Quality Improvement Project that utilized education, Best Practice Advisory (BPA) and modification of order sets in the electronic health record (EHR). The interventions were done in a sequential order based on the Plan-Do-Study-Act (PDSA) method. An annotated run chart was used to analyze the collected data. Education and Best Practice Advisory within the EHR led to significant reduction in the use of FOBT in the ED. The interventions eventually led to a consensus and removal of FOBT from the order set of the EHR for patients in the ED and hospital units. The use of electronic BPA, education and modification of order sets in the EHR can be effective at de-implementing unnecessary tests and procedures like FOBT in the ED and hospital units.

Key interoperability Factors for patient portals and Electronic health Records: A scoping review

AimTo identify the key requirements and challenges to interoperability between patient portals and electronic health records (EHRs). IntroductionPatient portals provide patients with access to their health information directly from EHRs within hospitals, primary care centres and general practices (GPs). Patient portals offer many benefits to patients including improved communication with healthcare providers and care coordination. However, many challenges exist with the integration and automatic and secure sharing of information between EHRs and patient portals. It is critical that countries learn from international experiences to successfully develop interoperable national patient portals. MethodsA scoping review methodology was undertaken. A search strategy using index terms and keywords was applied across four key databases, an additional grey literature search was also run. The identified studies were screened by two reviewers to determine eligibility against defined inclusion criteria. Data were abstracted from the eligible studies and reviewed to identify the key requirements and challenges to interoperability of patient portals with EHRs. ResultsAfter screening 3,462 studies, 34 were included across 11 countries. Of the 29 unique patient portals studied, few offered patients access to their entire healthcare record across multiple sites and a number of different functionalities were available. Key interoperability requirements and challenges identified were: Data Sharing Incentives & Supports; Heterogenous Organisations & Information Systems; Data Storage & Management; Available Information & Functionalities; Data Formats & Standards; Identification of Individuals; User Access, Control & Consent; and Security & Privacy. ConclusionSeamless exchange of health information across patient portals and EHRs required organisational and individual factors, as well as technical considerations. Interorganisational collaboration and engagement of key stakeholders to determine standards and guidelines for consent and sharing of information, as well as technical standards and security measures were recommended.

Beyond Hospital-Level Aggregated Data: A Methodology to Adapt Clinical Data From the Electronic Health Record for Nursing Unit-Level Research.

Studies of nurse staffing frequently use data aggregated at the hospital level that do not provide the appropriate context to inform unit-level decisions, such as nurse staffing. Describe a method to link patient data collected during the provision of routine care and recorded in the electronic health record (EHR) to the nursing units where care occurred in a national dataset. We identified all Veterans Health Administration acute care hospitalizations in the calendar year 2019 nationwide. We linked patient-level EHR and bar code medication administration data to nursing units using a crosswalk. We divided hospitalizations into segments based on the patient's time-stamped location (ward stays). We calculated the number of ward stays and medication administrations linked to a nursing unit and the unit-level and facility-level mean patient risk scores. We extracted data on 1117 nursing units, 3782 EHR patient locations associated with 1,137,391 ward stays, and 67,772 bar code medication administration locations associated with 147,686,996 medication administrations across 125 Veterans Health Administration facilities. We linked 89.46% of ward stays and 93.10% of medication administrations to a nursing unit. The average (standard deviation) unit-level patient severity across all facilities is 4.71 (1.52), versus 4.53 (0.88) at the facility level. Identification of units is indispensable for using EHR data to understand unit-level phenomena in nursing research and can provide the context-specific information needed by managers making frontline decisions about staffing.

Real world challenges in maintaining data integrity in electronic health records in a cancer program

Electronic Health Record (EHR) systems increase clerical workload, promote copy-paste and error propagation. Documentation error rate in cancer diagnosis and treatment was examined in 776 patient records. Fifteen percent of the charts contained an error. Modern EHR systems, patient portals and engagement tools may facilitate the maintenance of accurate information.

An Access Control Scheme with Privacy-preserving Authentication and Flexible Revocation for Smart Healthcare.

IoT and 5G-enabled smart healthcare allows medical practitioners to diagnose patients from any location via electronic health records (EHRs) by wireless body area network (WBAN) devices. Privacy, including the medical practitioner's identity and the patient's EHR, can easily be leaked from hospitals or cloud servers, and secret keys used to access EHRs must be revoked after diagnosis. In response to the challenges associated with user authentication and secret key revocation, this paper proposes an access control scheme with privacy-preserving authentication and flexible revocation for smart healthcare using attribute-based encryption (ABE), named PAFR-ABE, which provides access control to prevent malicious users from decrypting EHRs. Meanwhile, PAFR-ABE ensures privacy-preserving authentication for users during secret key generation, safeguarding users' identities and preventing unauthorized requests for secret keys. In addition, PAFR-ABE achieves flexible revocation and recovery of secret keys, eliminating the need to update secret keys for unrevoked users. Security analysis indicates that PAFR-ABE meets the security requirements of an access control scheme for smart healthcare, especially in terms of forward security and backward security. Performance analysis shows that PAFR-ABE scheme is efficient in the key generation and revocation algorithms.

Merkle-Damgård hash functions and blockchains : Securing electronic health records

The integration of Blockchain (BC) technology with the Merkle-Damgård Structure (M-DS) presents a practical model for securing Electronic Health Records (EHR). This research study examines how these cryptographic mechanisms can be functional to address confidentiality, integrity, and availability concerns when working with specific clinical data. Secure EHR is developed based on the M-DS, which is notable for being capable of generating Collision-Resistant Hash Functions (CRHF). The summary of BC-based technology in the EHR environment enhances access control, allows it to be fixed, and simplifies decentralized access. A decentralized BC system stores the values that have been hashed, resulting in patient EHR using the M-DC, as per the proposed method. As an outcome of smart contracts’ ability to manage access, only persons who have been authorized to access can analyze and alter clinical information. The strategy of the model’s cryptographic mechanism highlights openness and accountability by securing data confidentiality by encryption and by regularly generating a verification record of every transaction. This work discusses an exhaustive method to secure EHR in a clinical system that develops more computer-aided and data-driven over performing a complete assessment of the mathematical models that are enabling MDC and BC Technology within the overall system of EHR security.

Quantifying and Reducing Errors in Vascular Imaging Examination Orders Through a Multistage Quality Improvement Intervention

PurposeThe aims of this study were to quantify order error rates for vascular imaging examinations and to assess the effects of a multistage quality improvement intervention on those rates. MethodsIn this prospective, institutional review board–exempt project at a large academic quaternary care hospital, the authors aimed to quantify and reduce the order error rate by 50%. The authors analyzed 844 orders for all vascular imaging examinations placed before the intervention (July 19 to August 1, 2021, and September 13 to September 26, 2021), after an intervention in the cardiac surgery department consisting of a new customized order option in the electronic health record for routine preoperative patients (postintervention 1, February 28 to March 27, 2022); and after an educational and feedback campaign (postintervention 2, May 23 to June 5, 2022). Incorrect orders were identified by a radiology trainee during protocoling if the reasons for ordered examination and imaging examination were discordant and subsequently confirmed with the ordering provider. The primary outcome, order error rate, was compared across the project periods using the χ2 test and by ordering department using the χ2 and Fisher exact tests. ResultsThe preintervention order error rate of 16% (50 of 306) decreased by 83% to 3% (10 of 353) at postintervention 1 (P < .001) and was durable at 3% (6 of 185) by project end. Chest CT with or without contrast constituted the majority of incorrect orders (44%, 22 of 50); “Pre-Op” was the most common examination reason (32% [16 of 50]). Cardiac surgery orderers were responsible for the most incorrect orders (32% [16 of 50]). All four most common ordering departments, including cardiac surgery, reduced their order error rates after the intervention (P < .001). ConclusionsIncorrect orders for imaging examinations can be reduced through targeted quality improvement interventions combining tailored electronic health record order options with education and feedback on practice habits.

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.

To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH). This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models. The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27)years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05. Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.