Electronic Healthcare Record Systems Research Articles

Generating Complex Explanations for Artificial Intelligence Models: An Application to Clinical Data on Severe Mental Illness.

We present an explainable artificial intelligence methodology for predicting mortality in patients. We combine clinical data from an electronic patient healthcare record system with factors relevant for severe mental illness and then apply machine learning. The machine learning model is used to predict mortality in patients with severe mental illness. Our methodology uses class-contrastive reasoning. We show how machine learning scientists can use class-contrastive reasoning to generate complex explanations that explain machine model predictions and data. An example of a complex class-contrastive explanation is the following: "The patient is predicted to have a low probability of death because the patient has self-harmed before, and was at some point on medications such as first-generation and second-generation antipsychotics. There are 11 other patients with these characteristics. If the patient did not have these characteristics, the prediction would be different". This can be used to generate new hypotheses, which can be tested in follow-up studies. Diuretics seemed to be associated with a lower probability of mortality (as predicted by the machine learning model) in a group of patients with cardiovascular disease. The combination of delirium and dementia in Alzheimer's disease may also predispose some patients towards a higher probability of predicted mortality. Our technique can be employed to create intricate explanations from healthcare data and possibly other areas where explainability is important. We hope this will be a step towards explainable AI in personalized medicine.

Rates of Reproductive Counseling and Contraception Use in Patients with Heart Failure at a Tertiary Care Center

BackgroundIncreasing numbers of women of childbearing age have cardiac disease, including heart failure (HF). In these women, pregnancy can cause significant morbidity and mortality. Contraceptive use and pregnancy counseling in women with HF is an essential part of their medical care. Here, we assess contraceptive use and pregnancy counseling of patients with HF at a single tertiary care center. Methods and ResultsThis was a retrospective, single-center cohort study of female patients with HF with reduced ejection fraction, left ventricular assist devices (LVADs), and heart transplants who were seen in the adult advanced HF outpatient clinics. Patients were identified in the electronic health care record system, and records were reviewed to assess for documentation of contraception and pregnancy counseling. We identified 156 women of childbearing age (aged >18 to <45), seen in the HF clinics between 2018 and 2023. Patients were subdivided by their most recent diagnosis and therapy: HF with reduced ejection fraction (83 [53.2%]), LVAD (18 [11.5%]), and heart transplant (55 [35.3%]). Contraception was documented for 74% of women with HF, 56% of women with LVAD, and 85% of women with heart transplants. Pregnancy counseling was documented for 18.00% of women with HF, 0.06% of women with LVAD, and 29.00% of women with heart transplants. ConclusionsIn our study, many women with HF, LVAD, or transplant have documented contraceptive therapy; however, pregnancy counseling seems to be limited. This vital aspect of medical care should be available for all patients given potential pregnancy-associated risks.

Clinical Features and Prognostic Factors in Multiple Myeloma Patients from Colombia. Real-World Data from Renehoc Registry

Background: The prognosis for patients with multiple myeloma (MM) has improved over time, but there is considerable heterogeneity in disease outcomes. Currently used risk models have been validated using data from patients in clinical trials, but their applicability to real-world populations may be limited. To provide clinicians with better tools for decision-making, it is essential to identify factors associated with overall survival (OS) in a local setting. This study aims to identify clinical factors associated with OS in a real-world cohort of Colombian patients with newly diagnosed multiple myeloma (NDMM) using data from the registry on hematologic diseases (RENEHOC). Methods: RENEHOC is an electronic healthcare record (EHR) system comprising data from 25 active centers in 6 Colombian cities as of July 2023. The study population was characterized using descriptive methodology. Two cohorts of patients were defined: Cohort A included patients who died within 5 years after diagnosis, and Cohort B included patients who were either alive or censored 5 years after diagnosis. Variables with a high proportion of missing information were evaluated in terms of magnitude and proportion. Univariate logistic regression was used to identify variables associated with OS, with those having a P value less than 0.2 considered for multivariate analysis. The results were presented as odds ratios (OR) with 95% confidence intervals (CI), and P values were calculated using the LR-test. Results: The analysis included 1579 patients with NDMM. The median age was 67 years (IQR 60-75), and 51.1% (n=808) of the patients were male. Most patients were insured through the contributory health system plan. At diagnosis, 36.9% (n=582) of patients had a bone fracture (n=425) and 26.9% (n=425) had renal insufficiency, with 9.8% requiring dialysis. Cytogenetic analysis was performed in 22.9% (n=361) of patients, and 24.1% (n=87) of them had high-risk features. Half of the patients were classified as ISS III at diagnosis. The median OS for the entire cohort was 98.4 months. According to the international staging system (ISS), OS varied significantly: 106.2 months for ISS I, and 79.1 and 63.1 months for ISS II and ISS III, respectively (p &amp;lt; 0.001). First-line treatment for 90% (n=1407) of patients included chemotherapy, with 70% of patients treated with Bortezomib-based triplets, with VCD (Bortezomib, Cyclophosphamide, and Dexamethasone) being the most frequently used of them (30.7%, n=486). Only 21.7% of patients were consolidated with autologous stem cell transplantation (ASCT) in the first line. Cohort A comprised 341 (21.6%) patients who died within 5 years after diagnosis. Univariate analysis showed a significantly higher proportion of patients ≥65 years, from the subsidized health plan, with renal insufficiency requiring dialysis, elevated B2-microglobulin levels at diagnosis, and who did not receive ASCT in this cohort. After multivariate logistic regression, four variables remained statistically significant predictors of OS: age ≥65 years, elevated B2-microglobulin levels, not receiving ASCT, and higher albumin values at diagnosis. These variables were associated with higher mortality risk, except for higher albumin values, which were associated with better survival (Table I). Conclusions: This study contributes to the growing body of evidence on multiple myeloma prognosis in the Colombian context. These findings provide valuable insights for clinicians in the local setting and may aid them in making informed decisions for individual patients. Nevertheless, future research and validation studies are warranted to confirm and build upon these findings.

Abstract 17845: Logic-Based Natural Language Processing Identifies VT Recurrence in the Electronic Health Record

Introduction: With the advent of integrated electronic healthcare record (EHR) systems, it is increasingly important to identify patients who may suffer a clinical endpoint to allocate resources and develop predictive models. For the endpoint of VT recurrence after ablation, outcomes are currently assessed manually by experts, which is time consuming with few automated tools. We set out to develop a robust natural language processing model to parse cardiology and electrophysiology notes to detect VT. Hypothesis: We hypothesize that natural language processing (NLP) with logic-based algorithms can be used to accurately extract specific information on VT recurrence from complex clinical notes, compared to experts. Methods: We studied N=639 full-text clinical notes written by board certified cardiologists from N = 125 patients (32.0% female, LVEF 48.9±13.9%, 61±14.0 years). Notes were labeled as “No” or “Yes” for VT recurrence, and split into a development set of N=303 (median word count = 236) from N=75 patients and a test set of N=336 (median words = 297) from N=50 patients. We developed an NLP rule based expert system that uses hierarchical groups of keywords to identify sentences related to VT, then uses negation and quantifier functions to interpret context and assign a label. Results: The logical flow is shown in figure A. This approach achieved an 93.73% accuracy in the development set (precision = 57.14%, recall = 69.56%), and 90.48% accuracy (precision = 36.84%, recall = 63.63%) in the test set (figure B). Conclusions: An NLP logic-based approach can identify VT recurrence post ablation from complex clinical notes. NLP can thus be used to accelerate data extraction for clinical purposes or for semi-supervised learning. Future work will determine if logic-based NLP need to be augmented for noisier data, such as the entire EHR system.

Health chain-block chain based electronic healthcare record system with access and permission management

An electronic health record (EHR) is a technology that allows you to keep track of your health information. It keeps computerized records of several healthcare organizations. Records are exchanged via enterprise-wide data systems as well as other networking technologies and exchanges. Patients nowadays expect immediate access to their health information. However, the health sector comes with immediate access to data, and there are worries about the privacy and security of medical records of patients. As a result, a blockchain-based solution can assist in resolving this issue. The blockchain has the potential to beat the conventional centralized system, which suffers from a severe lack of accessibility. The current healthcare system is often seen as being both costly and convoluted. However, with improved health data management and insurance providers, this may be reduced via the use of Blockchain Technology (BT). It is the goal of the developers of BT to make it possible to keep track of financial transactions without having to rely on a central authority or a bank. Innovations in BT have allowed for better health records, insurance billing, and smart contract transactions, and it also enable a decentralized data structure of activities, as well as eternal availability and security over health information. Access to medical records, instrument tracking, prescription databases, hospital facilities, and property lifetime all inside a blockchain architecture is made possible by BT that also greatly improves the seamless integration of health information. The use of BT in the medical field has this advantage as one of its primary advantages. Access to a patient's medical history is crucial for prescribing the right treatment, and BT has the potential to greatly enhance the current state of public healthcare delivery. This paper offers a block chain-based scheme called “Health Chain” as a solution for maintaining medical records. Health Chain is made to be better than the existing systems since it gives patients, health professionals, and other external stakeholders safe, efficient, and ubiquitous medical information available while maintaining the patients' confidentiality. To regulate operations and restrict access to digital health records, Health Chain uses timed-based smart contracts. In addition, it uses cutting-edge encryption methods to boost security. This study suggests a new reward system that uses healthcare professionals to track patients' medical information and construct additional units. Numerous tests are run to assess the proposed Health Chain model's performance, and the findings demonstrate that the proposed suggestion effectively manages a huge dataset called (nCOV19) from the Covid-19 study with little delay. Maximum efficiency will be expected to be attained by 98%, transaction latency by 95%, overall system execution time by 96%, data security by 92%, and data scalability by 95%, according to the suggested research.

Augmented intelligence facilitates concept mapping across different electronic health records

IntroductionWith the advent of artificial intelligence, the secondary use of routinely collected medical data from electronic healthcare records (EHR) has become increasingly popular. However, different EHR systems typically use different names for the same medical concepts. This obviously hampers scalable model development and subsequent clinical implementation for decision support. Therefore, converting original parameter names to a so-called ontology, a standardized set of predefined concepts, is necessary but time-consuming and labor-intensive. We therefore propose an augmented intelligence approach to facilitate ontology alignment by predicting correct concepts based on parameter names from raw electronic health record data exports. MethodsWe used the manually mapped parameter names from the multicenter “Dutch ICU data warehouse against COVID-19” sourced from three types of EHR systems to train machine learning models for concept mapping. Data from 29 intensive care units on 38,824 parameters mapped to 1,679 relevant and unique concepts and 38,069 parameters labeled as irrelevant were used for model development and validation. We used the Natural Language Toolkit (NLTK) to preprocess the parameter names based on WordNet cognitive synonyms transformed by term-frequency inverse document frequency (TF-IDF), yielding numeric features. We then trained linear classifiers using stochastic gradient descent for multi-class prediction. Finally, we fine-tuned these predictions using information on distributions of the data associated with each parameter name through similarity score and skewness comparisons. ResultsThe initial model, trained using data from one hospital organization for each of three EHR systems, scored an overall top 1 precision of 0.744, recall of 0.771, and F1-score of 0.737 on a total of 58,804 parameters. Leave-one-hospital-out analysis returned an average top 1 recall of 0.680 for relevant parameters, which increased to 0.905 for the top 5 predictions. When reducing the training dataset to only include relevant parameters, top 1 recall was 0.811 and top 5 recall was 0.914 for relevant parameters. Performance improvement based on similarity score or skewness comparisons affected at most 5.23% of numeric parameters. ConclusionAugmented intelligence is a promising method to improve concept mapping of parameter names from raw electronic health record data exports. We propose a robust method for mapping data across various domains, facilitating the integration of diverse data sources. However, recall is not perfect, and therefore manual validation of mapping remains essential.

Evaluating REDCap as the Central Data Collection Tool for the Hamburg City Health Study.

The collection of examination data for large clinical studies is often done with proprietary systems, which are accompanied by several disadvantages such as high cost and low flexibility. With the use of open-source tools, these disadvantages can be overcome and thereby improve data collection as well as data quality. Here we exemplary use the data collection process of the Hamburg City Health Study (HCHS), carried out at the University Medical Center Hamburg-Eppendorf (UKE). We evaluated how the recording of the examination data can be converted from an established, proprietary electronic healthcare record (EHR) system to the free-to-use Research Electronic Data Capture (REDCap) software. For this purpose, a technical conversion of the EHR system is described first. Metafiles derived from the EHR system were used for REDCap electronic case report form (eCRF) building. The REDCap system was tested by HCHS study assistants via completion of self-developed tasks mimicking their everyday study life. Usability was quantitatively evaluated via the IBM Computer System Usability Questionnaire (CSUQ) and qualitatively assessed with a semi-structured interview. With the IBM CSUQ, the study assistants rated the usage of the basic REDCap system for HCHS examination data collection with an overall score of 4.39, which represents a medium acceptance. The interview feedback was used to formulate user stories to subsequently increase the administrative sovereignty and to conceptualize a REDCap HCHS information technology (IT) infrastructure. Our work aims to serve as a template for evaluating the feasibility of a conversion from a proprietary to a free-to-use data collection tool for large clinical studies such as the HCHS. REDCap has great potential, but extensions and an integration to the current IT infrastructure are required.

The Complexities of Mechanical Ventilation: Toppling the Tower of Babel.

The exponential increase in the complexity of ventilator technology has created a growing knowledge gap that hinders education, research, and ultimately the quality of patient care. This gap is best addressed with a standardized approach to educating clinicians, just as education for basic and advanced life support classes is standardized. We have developed such a program, called Standardized Education for Ventilatory Assistance (SEVA), based on a formal taxonomy for modes of mechanical ventilation. The SEVA program is a progressive system of 6 sequential courses starting from an assumption of no prior knowledge and proceeding to full mastery of advanced techniques. The vision of the program is to provide a unique platform for standardizing training by unifying the concepts of physics, physiology, and technology of mechanical ventilation. The mission is to use both online and in-person simulation-based instruction that has both self-directed and instructor-led components to elevate the skills of health care providers to the mastery level. The first 3 levels of SEVA are free and open to the public. We are developing mechanisms to offer the other levels. Spinoffs of the SEVA program include a free smartphone app that classifies virtually all modes on all ventilators used in the United States (Ventilator Mode Map), a free biweekly online training sessions focusing on waveform interpretation (SEVA-VentRounds), and modifications to the electronic health care record system for entering and charting ventilator orders.

Developing a reflection and analysis tool (We-ReAlyse) for readmissions to the intensive care unit: A quality improvement project

BackgroundReadmissions to the intensive care unit are associated with poorer patient outcomes and health prognoses, alongside increased lengths of stay and mortality risk. To improve quality of care and patients’ safety, it is essential to understand influencing factors relevant to specific patient populations and settings. A standardized tool for systematic retrospective analysis of readmissions would help healthcare professionals understand risks and reasons affecting readmissions; however, no such tool exists. PurposeThis study’s purpose was to develop a tool (We-ReAlyse) to analyze readmissions to the intensive care unit from general units by reflecting on affected patients’ pathways from intensive care discharge to readmission. The results will highlight case-specific causes of readmission and potential areas for departmental- and institutional-level improvements. MethodA root cause analysis approach guided this quality improvement project. The tool’s iterative development process included a literature search, a clinical expert panel, and a testing in January and February 2021. ResultsThe We-ReAlyse tool guides healthcare professionals to identify areas for quality improvement by reflecting the patient's pathway from the initial intensive care stay to readmission. Ten readmissions were analyzed by using the We-ReAlyse tool, resulting in key insights about possible root causes like the handover process, patient's care needs, the resources on the general unit and the use of different electronic healthcare record systems. ConclusionsThe We-ReAlyse tool provides a visualization/objectification of issues related to intensive care readmissions, gathering data upon which to base quality improvement interventions. Based on the information on how multi-level risk profiles and knowledge deficits contribute to readmission rates, nurses can target specific quality improvements to reduce those rates. Implications for clinical practice and researchWith the We-ReAlyse tool, we have the opportunity to collect detailed information about ICU readmissions for an in-depth analysis. This will allow health professionals in all involved departments to discuss and either correct or cope with the identified issues. In the long term, this will allow continuous, concerted efforts to reduce and prevent ICU readmissions. To obtain more data for analysis and to further refine and simplify the tool, it may be applied to larger samples of ICU readmissions. Furthermore, to test its generalizability, the tool should be applied to patients from other departments and other hospitals. Adapting it to an electronic version would facilitate the timely and comprehensive collection of necessary information. Finally, the tool’s emphasis comprises reflecting on and analyzing ICU readmissions, allowing clinicians to develop interventions targeting the identified problems. Therefore, future research in this area will require the development and evaluation of potential interventions.

Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system.

Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligence-driven tools for early detection. The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008-July 2020). Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model. In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group. In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92). Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems. This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence-driven early detection of iNPH and prompt treatment.

S307 Artificial Intelligence Identifies High Risk Patients Lost to Colon Cancer Screening Follow-Up During COVID-19 Pandemic

Introduction: The COVID-19 pandemic resulted in the complete stoppage of many colon cancer screening programs. During this time, many patients were at risk of being lost to follow up for their colorectal cancer (CRC) screening and surveillance. Here, we describe use of an artificial intelligence driven recall system to surface high risk patients potentially overdue for repeat colonoscopy and interrogate the reasons for missed recall. Methods: We conducted a retrospective study at a tertiary care academic medical center in continuous patients who underwent an initial colonoscopy between August to October 2019 and had a follow-up recommendation for a repeat colonoscopy within 2 years of the index procedure. A natural language understanding workflow was developed by Halo Solutions LLC in which: 1) procedure reports from gGastro (Modernizing Medicine, Florida) were converted to text files using OCR, and 2) these reports were evaluated alongside linked electronic healthcare record (EHR) data to determine the timing and indication of the recall procedure. Data was then manually reviewed to assess the reasons for missing the surveillance colonoscopy. Cases were randomly checked for accuracy, reaching an accuracy rate of 96%. Results: 4663 colonoscopies were performed, of which 14% (n=677) had a recall recommendation for surveillance colonoscopy within 2 years of the index colonoscopy. Of those cases, 24% (n=162/677) were flagged as potentially overdue. Of the 162 cases, 48 were found to have missing colonoscopy orders, 31 were not contacted by scheduling, and 32 were lost to follow up despite at least 1 attempted outreach. The remaining 51 were also lost to follow up but upon further review, deemed not overdue following manual review of the full record (e.g., outside records documented colonoscopy, patient expired, etc.). Conclusion: Missed CRC surveillance for patients with higher risk findings or disease processes increases the risk of patient harm from missed polyps and cancers. Nearly one in six patients who were advised to have a colonoscopy within two years of their index procedure were lost to follow up. In most EHR systems, once these patients are lost to follow up, there is no safety net to identify who these patients are. Integrating artificial intelligence into the EHR and clinical practice enables rapid identification of patients at risk of loss to follow up and allows for optimizing patient care and safety.

Blockchain and cloud computing-based secure electronic healthcare records storage and sharing.

Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.

Structural and Semantic Mapping of Application Programming Interfaces.

Modern healthcare providers rely upon Electronic Healthcare Records (EHR) systems to record patient data inside their own organization. Some healthcare providers share this data to facilitate patient care with other providers. Medical devices and healthcare providers can use differing standards of recording healthcare information. The Structural and Semantic Mapper Proxy API solution offers a practical way to tackles the issues of Structural and Semantic mapping of Application Programing Interfaces (API) in a healthcare context to enable connection of all existing systems to a healthcare providers EHR creating a single source of truth regarding the treatment of patients and enabling healthcare providers to bridge the gap between external EHR systems.

A Controllable Secure Blockchain-Based Electronic Healthcare Records Sharing Scheme.

The sharing of electronic healthcare records (EHRs) is important to healthcare and medical research. However, institutions are faced with difficulties in privacy protection and efficiently secure data exchange. The main objective of this study is to propose a controllable secure blockchain-based EHRs sharing scheme. For this purpose, blockchain technologies are combined with interplanetary file systems (IPFS) to provide efficient secure EHRs sharing. Firstly, the IPFS-based EHR file system (IEFS) is designed to save and share large-size EHR files among medical institutions. With the high-throughput content-addressed block storage model and appropriate redundant backup of IPFS, IEFS is tamper-resistant and free of a single point of failure. Secondly, the blockchain is used to implement the blockchain-based EHR abstract system (BEAS) to manipulate EHR abstracts access. In BEAS, the EHR file addresses generated by IEFS are encrypted and saved in EHR abstracts for privacy protection. Since EHR abstracts are encrypted by patients' public keys, the sharing of EHR files is under the control of patients. In our experiment, a prototype system is developed to validate the proposed scheme. The experimental results showed that (1) EHRs are securely shared under the control of patients and (2) EHR files are retrieved at an acceptable speed supported by IPFS technology. In this paper, solutions to some important practical issues such as incapacitated patients, encryption key forgetting/missing, and efficient interaction of doctors with EHRs sharing scheme are also seriously discussed.

An architectural design study of electronic healthcare record systems with associated context parameters on MIMIC III

An architectural design study of electronic healthcare record systems with associated context parameters on MIMIC III

A Blockchain and NLP Based Electronic Health Record System: Indian Subcontinent Context

The healthcare system in the Indian subcontinent is plagued with numerous issues related to the access, transfer, and storage of patient's medical records. The lack of infrastructure to properly communicate and track records between all key participants has allowed the distribution of counterfeit drugs, dependency on unsafe methods of communication, and lack of trust between patients and providers. During the global COVID-19 pandemic, the need for a robust communication and record tracking system has been further emphasized. To facilitate efficient communication and mitigate the mentioned issues, a nationwide EHR (electronic health record) system must be introduced to bring the healthcare system into digital space. To further enhance security, efficiency, and cost, the innovation of Blockchain is introduced. Blockchain is a decentralized data structure that allows secure transactions between untrusted parties without needing a central authority. In this paper, a Hyperledger fabric-based Blockchain Electronic Healthcare Record (EHR) system is proposed. The system is integrated with technologies such as NLP (Natural Language Processing), and Machine Learning to provide users with practical features.

Management of cardiovascular disease using an mHealth tool: a randomized clinical trial

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death and morbidity worldwide. This randomized controlled, single-center, open-label trial tested the impact of a mobile health (mHealth) service tool optimized for ASCVD patient care. Patients with clinical ASCVD were enrolled and randomly assigned to the intervention or control group. Participants in the intervention group were provided with a smartphone application named HEART4U, while a dedicated interface integrated into the electronic healthcare record system was provided to the treating physicians. A total of 666 patients with ASCVD were enrolled, with 333 patients in each group. The estimated baseline 10-year risk of cardiovascular disease was 9.5% and 10.8% in the intervention and control groups, respectively, as assessed by the pooled cohort risk equations. The primary study endpoint was the change in the estimated risk at six months. The estimated risk increased by 1.3% and 1.1%, respectively, which did not differ significantly (P = 0.821). None of the secondary study endpoints showed significant differences between the groups. A post-hoc subgroup analysis showed the benefit was greater if a participant in the intervention group accessed the application more frequently. The present study demonstrated no significant benefits associated with the use of the mHealth tool in terms of the predefined study endpoints in stable patients with ASCVD. However, it also suggested that motivating patients to use the mHealth tool more frequently may lead to greater clinical benefit. Better design with a positive user experience needs to be considered for developing future mHealth tools for ASCVD patient care.Trial Registration: ClinicalTrials.gov NCT03392259

Integrated electronic health record facilitates a safer and more efficient rural outreach haematology service.

Rural Australian oncology patients are known to have inferior mortality rates compared to metropolitan patients, possibly related to access to appropriate healthcare services and treatments. Electronic systems improve the safety of chemotherapy administration and allow easily accessible patient information and data collection. To integrate the electronic healthcare delivery systems at a metropolitan hospital and a rural outreach haematology clinic to facilitate streamlined and safe outpatient care. The MOSAIQ v2.64(Elekta) system utilised at St Vincent's Hospital was introduced at a linked rural outreach haematology clinic. The two separate comprehensive practice management systems incorporating all patient information were consolidated into one, becoming accessible from both sites. The electronic systems were successfully integrated between the two sites in October 2017. Electronic chemotherapy prescribing at the Griffith site is now guided by inbuilt, pharmacist-reviewed protocols thereby improving the safety and flexibility of remote prescribing. The centralised electronic health record has improved streamlined care during patient transitions between the two hospitals with enhanced continuity of documentation and management. Increases in total clinic patients and appointment numbers are demonstrable since implementation, and sustained during the COVID-19 pandemic. The present study provides a novel example of the successful implementation of a centralised electronic healthcare record and chemotherapy prescribing system in a haematology setting shared between a metropolitan service and a rural outreach hospital clinic. This has positive implications for the safety and efficiency of healthcare delivery at the rural site applicable to all linked rural Australian clinics, as well as allowing data collection to assist future planning of the service.

Why Was There Ever a Warning Not to Use Cephalosporins in the Setting of a Penicillin “Allergy”?

It is now well-established that avoiding β-lactam antibiotics, when they are the drugs of choice, results in significantly worse long-term global outcomes for patients. Much of this avoidable morbidity has been caused by widespread warnings in electronic health care record systems not to use cephalosporins in the setting of penicillin allergy. High rates, up to 1000-fold higher than now seen, of immunologically mediated reactions were associated with early impure penicillin preparations. This instilled a rational fear of β-lactam-associated anaphylaxis in generations of physicians. In the late 1970s, several editorial comments regarding a potential increased risk to patients given cephalosporins who had a history of a penicillin allergy resulted in the warning that became imbedded in the culture of medicine. Over the past 40 years, compelling data have been developed that refute this warning and showed that the risks of avoiding cephalosporins outweighed the benefits. In late 2017, Kaiser Permanente Southern California completely removed all warnings not to use cephalosporins in the setting of a penicillinallergy. The results have recently been published in JAMA Network Open. This Rostrum article provides some of thebackstory on the establishment and removal of this warningforphysicians who trained over the past 30 years.

Systematic review of applied usability metrics within usability evaluation methods for hospital electronic healthcare record systems: Metrics and Evaluation Methods for eHealth Systems.

Background and objectivesElectronic healthcare records have become central to patient care. Evaluation of new systems include a variety of usability evaluation methods or usability metrics (often referred to interchangeably as usability components or usability attributes). This study reviews the breadth of usability evaluation methods, metrics, and associated measurement techniques that have been reported to assess systems designed for hospital staff to assess inpatient clinical condition.MethodsFollowing Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) methodology, we searched Medline, EMBASE, CINAHL, Cochrane Database of Systematic Reviews, and Open Grey from 1986 to 2019. For included studies, we recorded usability evaluation methods or usability metrics as appropriate, and any measurement techniques applied to illustrate these. We classified and described all usability evaluation methods, usability metrics, and measurement techniques. Study quality was evaluated using a modified Downs and Black checklist.ResultsThe search identified 1336 studies. After abstract screening, 130 full texts were reviewed. In the 51 included studies 11 distinct usability evaluation methods were identified. Within these usability evaluation methods, seven usability metrics were reported. The most common metrics were ISO9241‐11 and Nielsen's components. An additional “usefulness” metric was reported in almost 40% of included studies. We identified 70 measurement techniques used to evaluate systems. Overall study quality was reflected in a mean modified Downs and Black checklist score of 6.8/10 (range 1–9) 33% studies classified as “high‐quality” (scoring eight or higher), 51% studies “moderate‐quality” (scoring 6–7), and the remaining 16% (scoring below five) were “low‐quality.”ConclusionThere is little consistency within the field of electronic health record systems evaluation. This review highlights the variability within usability methods, metrics, and reporting. Standardized processes may improve evaluation and comparison electronic health record systems and improve their development and implementation.