EHR Systems Research Articles

Usability of Electronic Health Records in Germany - An Overview of Satisfaction of University Hospital Physicians.

EHR are a part of daily task of physicians in Germany. This study surveyed the satisfaction of a small group of physicians in German university hospitals using EHR with focus on usability. The questioning was carried out by an online survey. Addressed were all physicians working at university hospitals in Germany. The study showed that users are not satisfied with EHR (Grade 3.62). They pointed out some problems in general but reflected many advantages of those systems. EHR systems have to develop and adopt to users' tasks. They have to get faster and low error rates must be realized. Existing infrastructure must be improved and rolled out to users especially in times where digital healthcare services gain importance.

Identifying and adapting interventions to reduce documentation burden and improve nurses’ efficiency in using electronic health record systems (The IDEA Study): protocol for a mixed methods study

BackgroundAlthough EHR systems have become a critical part of clinical care, nurses are experiencing a growing burden due to documentation requirements, taking time away from other important clinical activities. There is a need to address the inefficiencies and challenges that nurses face when documenting in and using EHRs. The objective of this study is to engage nurses in generating ideas on how organizations can support and optimize nurses’ experiences with their EHR systems, thereby improving efficiency and reducing EHR-related burden. This work will ensure the identified solutions are grounded in nurses’ perspectives and experiences and will address their specific EHR-related needs.MethodsThis mixed methods study will consist of three phases. Phase 1 will evaluate the accuracy of the EHR system’s analytics platform in capturing how nurses utilize the system in real-time for tasks such as documentation, chart review, and medication reconciliation. Phase 2 consists of a retrospective analysis of the nursing-specific analytics platform and focus groups with nurses to understand and contextualize their usage patterns. These focus groups will also be used to identify areas for improvement in the utilization of the EHR. Phase 3 will include focus groups with nurses to generate and adapt potential interventions to address the areas for improvement and assess the perceived relevance, feasibility, and impact of the potential interventions.DiscussionThis work will generate insights on addressing nurses’ EHR-related burden and burnout. By understanding and contextualizing inefficiencies and current practices, opportunities to improve EHR systems for nursing professional practice will be identified. The study findings will inform the co-design and implementation of interventions that will support adoption and impact. Future work will include the evaluation of the developed interventions, and research on scaling and disseminating the interventions for use in different organizations, EHR systems, and jurisdictions in Canada.

Authorization and privacy preservation in cloud-based distributed ehr system using blockchain technology and anonymous digital ring signature

Authorization and privacy preservation in cloud-based distributed ehr system using blockchain technology and anonymous digital ring signature

Interoperability and Governance; Important Preconditions for Establishing Large-Scale Healthcare Infrastructures Like the Akson program in Norway.

In Norway there is an overall goal to establish a national digitalization platform for primary healthcare named Akson to improve information exchange. We participated in the work with Akson and through qualitative research including interviews, we found that the project could benefit from other similar infrastructuring processes. First, a national process of defining clinical standards and establishing a governance organization to handle them. Second, improving data exchange between an EHR system and a national quality registry. The aim of the paper is to outline some lessons learned from these previous processes, for Akson and similar large-scale projects focusing on how to govern the digitalization platform at different healthcare levels and how to reuse healthcare information within and across healthcare institutions. Hence, we ask the following research question: Which experiences from previous large-scale infrastructuring processes should be considered when establishing a national digitalization platform for sharing data?

OpenEHR Implementation Guide: Towards Standard Low-Code Healthcare Systems.

Several open source components have been made available in recent years to help develop full openEHR systems. Still doubts exist if these are sufficient. This paper presents a case study of implementing a low-code openEHR system, investigating the feasibility and challenges of developing a system using these components for each step. The method used consisted in selecting successful examples of implementation case studies, identifying key development steps, and for each step searching for possible open source options. As a result, we had a working low-code openEHR powered EHR, successfully demonstrating the feasibility of the proposed implementation guide. The main available free or open source components used were ArchetypeDesigner and EHRbase, developed by Better and Vita/HighMed respectively. In our opinion, it is possible to build EHR systems using the available open source components, but support is still missing in the front end, specifically for form generation and screen representation.

Annotating Music Therapy, Chiropractic and Aquatic Exercise Using Electronic Health Record.

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. The overall goal of this study is to represent information pertinent to music therapy, chiropractic and aquatic exercise in an EHR system. A total of 300 clinical notes were randomly selected and manually annotated. Annotations were made for status, symptom and frequency of each approach. This set of annotations was used as a gold standard to evaluate performance of NLP systems used in this study (specifically BioMedICUS, MetaMap and cTAKES) for extracting CIH concepts. Three NLP systems achieved an average lenient match F1-score of 0.50 in all three CIH approaches. BioMedICUS achieved the best performance in music therapy with an F1-score of 0.73. This study is a pilot to investigate CIH representation in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

Natural language processing-optimized case selection for real-world evidence studies.

1556 Background: Much information describing a patient’s cancer treatment remains in unstructured text in electronic health records and is not recorded in discrete data fields. Accurate data completeness is essential for quality care improvement and research studies on de-identified patient records. Accessing this high-value content often requires manual and extensive curation review. Methods: AstraZeneca, CancerLinQ, ConcertAI, and Tempus have developed a natural language processing (NLP)-assisted process to improve clinical cohort selection for targeted curation efforts. Hybrid, machine-learning model development included text classification, named entity recognition, relation extraction and false positive removal. A subset of nearly 60,000 lung cancer cases were included from the CancerLinQ database, comprised of multiple source EHR systems. NLP models extracted EGFR status, stage, histology, radiation therapy, surgical resection and oral medications. Based on the results, cases were selected for additional manual curation, where curators confirmed findings of the NLP-processed data. Results: NLP methods improved cohort identification. Successfully returned cases using the NLP method ranged from 75.2% to 96.5% over more general case selection criteria based on limited structured data. For all cohorts combined, 84.2% of the cases sent out for NLP curation were returned with curated content (Table). Each cohort contained a range of NLP-derived elements for curators to further review. In comparison, more general case selection criteria yielded a total of 3,878 cases returned out of 41,186 lung cancer cases sent for curation, for a success rate of only 9.6%. Conclusions: NLP-driven case selection of six distinct, complex lung cohorts resulted in an order of magnitude improvement in eligibility over candidate selection using structured EHR data alone. This study demonstrates NLP-assisted approaches can significantly improve efficiency in curating unstructured health data. [Table: see text]

Evaluation of a three-part equity intervention for women of color with breast cancer.

TPS1129 Background: Women of color (WOC) with breast cancer miss and fail key points in care due to racial disparities in cancer services. Conscious and unconscious bias means that these women are not treated in a timely way or as rigorously. They are offered fewer options, and the patient navigation and education needed for them to self-advocate is ignored. Black women in particular have the highest breast cancer death rates. Care delivery differs even independent of such variables as literacy, income, and education. Separately and incrementally, findings to date have shown the potential oof patient navigation, equity assessment, and mobile support to reverse these disparities. In pilot investigations –– partnering with academic cancer centers –– The Chrysalis Initiative (a nonprofit patient advocacy and research organization) has validated the potential of combining these approaches in a three-part intervention. Based on preliminary work, it is hypothesized patients will: experience significantly greater adherence to the recommended continuum of care, without disruption or barriers; more often seek second opinions and additional supportive resources, and engage in clinical trials; demonstrate less co-morbidities, through more preventive measures and healthful lifestyle; suffer fewer interactions perceived to be influenced by racism; undergo less financial distress, with guidance on managing and planning costs; feel more confident and more optimistic in their outlook; and achieve better clinical results and lower costs. Methods: To determine and document the full impact, both quantitatively and qualitatively, of the experimental three-part intervention, the trial is: delivering navigation/coaching services to the study population, using counselors who are predominantly WOC who have experienced the challenges of breast cancer care. Surveys and interviews pre- and post-intervention will add to impact measures; providing the experimental group with the BC Navi App on both iOS and Android devices . Developed in partnership with InTouch, the app supports engagement and tracking. It provides a dashboard for care evaluation to supplement EMR data; conducting an equity assessment of breast cancer services of partnering clinical programs through use of focus groups with staff, patients, and community; procedural checks; and data collection from center EHR systems. The review audits 40 domains of care based on NCCI, NCI, ASCO, ACR, and other standards. The assessment team works closely and collaboratively with each cancer center’s clinical and administrative staff to reveal disparities and find consensus on ways to close gaps. This trial in progress is randomizing 200 subjects to the coaching/mobile app intervention arm and 100 to benefits of the equity assessment only, with comparison to nonWOC with breast cancer. The trial aims to disseminate the three-part intervention in easily reproducible form.

Predicting Cardiovascular Disease in Psychiatric Patients: Machine Learning with Electronic Health Records

IntroductionCardiovascular disease (CVD) causes staggering losses in quality adjusted life years worldwide.1 Among patients in the Danish psychiatric hospital setting, heart disease is associated with a decrease in life expectancy of 5.1 years.2 The causes underlying this association are likely manifold. For example, severe mental illness is associated with unhealthy lifestyle.3 Furthermore, psychiatrists may focus predominantly on the treatment of mental illness and have less emphasis on detection and prevention of physical illness.4 If patients at elevated risk of CVD are pointed out automatically, this may lead to better preventive medicine.ObjectivesTo predict which patients develop cardiovascular disease using machine learning.MethodsWe obtained data on all psychiatric hospital contacts in the Central Denmark Region since the initiation of the current EHR system (MidtEPJ). These span from 2011 to 2021 and cover 120,000 patients, of which 3,000 patients developed severe CVD (stroke or coronary event) follow-up. We will train a variety of models (random forests, SVM, deep neural nets) to predict CVD within one year from a planned contact to hospital.ResultsThe modelling is currently underway, intermediary results are expected in January.ConclusionsWe explore whether predicting CVD is feasible using state-of-the-art technologies and a uniquely detailed dataset. This may pave the way for machine learning to act as a clinical support decision system, since we’re only training on data that is available in a live, clinical context.References1: Khan 20192: Erlangsen 20173: Scott 20114: Fagiolini 2009DisclosureNo significant relationships.

Developing and internally validating a prediction model for total knee replacement surgery in patients with osteoarthritis

ObjectiveThe objective of this study was to develop and internally validate a clinical algorithm for use in general practice that predicts the probability of total knee replacement (TKR) surgery within the next five years for patients with osteoarthritis. The purpose of the model is to encourage early uptake of first-line treatment strategies in patients likely to undergo TKR and to provide a cohort for the development and testing of novel interventions that prevent or delay the progression to TKR. MethodElectronic health records (EHRs) from 201,462 patients with osteoarthritis aged 45 years and over from 483 general practices across Australia were linked with records from the Australian Orthopaedic Association National Joint Replacement Registry and the National Death Index. A Fine and Gray competing risk prediction model was developed using these data to predict the risk of TKR within the next five years. ResultsDuring a follow-up time of 5 years, 15,979 (7.9%) patients underwent TKR and 13,873 (6.9%) died. Predictors included in the final algorithm were age, previous knee replacement, knee surgery (other than TKR), prescribing of osteoarthritis medication in the 12 months prior, comorbidity count and diagnosis of a mental health condition. Optimism corrected model discrimination was 0.67 (95% CI: 0.66 to 0.67) and model calibration acceptable. ConclusionThe model has the potential to reduce some of the economic burden associated with TKR in Australia. External validation and further optimisation of the algorithm will be carried out prior to implementation within Australian general practice EHR systems.

MedicalForms: Integrated Management of Semantics for Electronic Health Record Systems and Research Platforms

(1) Background: Clinical information modeling tools are software instruments designed to support the definition of semantic structures able to be implemented in health information systems. Based on the analysis of existing tools, this research developed a tool that proposes new approaches to promoting clinician involvement and supporting information modeling processes through mechanisms that ensure governance, information consistency and consensus building. (2) Method: This research developed the MedicalForms system, which is based on the requirements identified in both a Delphi study about tool requirements and the ISO/TS 13972 specifications. (3) Results: This system allows the management of projects, information structures and implementable forms related to clinical documentation. Users can easily define clinical documents in collaboration with the rest of the professionals in their team by being able to reuse previously defined forms, terminologies and information structures. The system is able to export the defined forms as interoperable specifications or as several implementable form formats compatible with multiple open source EHR systems and research platforms. End user perception of this tool was evaluated through the Technology Acceptance Questionnaire with satisfactory results. Finally, the system was applied to develop 12 research registries and 2 clinical trial research forms, 3 mobile applications and 1 decision support system.

Impact of longitudinal data-completeness of electronic health record data on risk score misclassification.

Electric health record (EHR) discontinuity, that is, receiving care outside of a given EHR system, can lead to substantial information bias. We aimed to determine whether a previously described EHR-continuity prediction model can reduce the misclassification of 4 commonly used risk scores in pharmacoepidemiology. The study cohort consists of patients aged ≥ 65 years identified in 2 US EHR systems linked with Medicare claims data from 2007 to 2017. We calculated 4 risk scores, CHAD2DS2-VASc, HAS-BLED, combined comorbidity score (CCS), claims-based frailty index (CFI) based on information recorded in the 365 days before cohort entry, and assessed their misclassification by comparing score values based on EHR data alone versus the linked EHR-claims data. CHAD2DS2-VASc and HAS-BLED were assessed in atrial fibrillation (AF) patients, whereas CCS and CFI were assessed in the general population. Our study cohort included 204 014 patients (26 537 with nonvalvular AF) in system 1 and 115 726 patients (15 529 with nonvalvular AF) in system 2. Comparing the low versus high predicted EHR continuity in system 1, the proportion of patients with misclassification of ≥2 categories improved from 55% to 16% for CHAD2DS2-VASc, from 55% to 12% for HAS-BLED, from 37% to 16% for CCS, and from 10% to 2% for CFI. A similar pattern was found in system 2. Using a previously described prediction model to identify patients with high EHR continuity may significantly reduce misclassification for the commonly used risk scores in EHR-based comparative studies.

Practical aspects of the electronic system functioning in the field of medicine of the future (Pakistan)

This article focused on “Practical aspects of the electronic system functioning in the field of medicine of the future in Pakistan” Aims: To explore the practical aspects of the electronic systems functioning in the field of medicine of the future, the challenges and opportunities of implementing these systems, and the potential solutions for optimising and enhancing their impact on outcomes in Pakistan. Methodology: A descriptive cross-sectional study design was used. A sample of 323 was selected through non-random convenient method. Data collected through interview. Chi square test was applied. Results: The results on telemedicine showed, out of 323 participants who practiced telemedicine, 52% had 19-24 months of medical practice experience. Among the doctors, 19.3% used laptops, 22.7% used mobile phones to search information online, while 41.1% connected with their patients through WhatsApp and 16.9% through Facebook. 80% of the doctors agreed that telemedicine was beneficial for saving money, time, effort, and for the patients. Among the group with experience above 24 months, 59% were practicing EHR systems and 41% were not utilising them. When it came to CDS Systems, 64.2% of the more experienced group were using the system, while 35.8% were not. In addition, 71.2% of the more experienced group were practicing Digital Medical Imaging. During the COVID-19 pandemic, all medical institutes taught, assigned tasks, and evaluated students through online mechanisms. However, after the pandemic, 100% of the institutes returned to traditional teaching methods. Interestingly, 60% of the participants favoured online education, considering it an easier, more effective and efficient way of learning. Scientific Novelty: Examines Pakistan's computerised healthcare system and its impacts. Conclusion: Improved infrastructure, healthcare education, patient computer literacy, privacy and data security rules may have the largest impact on healthcare outcomes. An international help is needed to reach global electronic health care standards. Note: Review paper may have different types of subsections.

Heterogeneous signcryption with proxy re-encryption and its application in EHR systems

Heterogeneous signcryption with proxy re-encryption and its application in EHR systems

A New Digital Platform for Collecting Measurement Data from the Novel Imaging Sensors in Urology.

The use of UT and EIT technologies gives the opportunity to develop new, effective, minimally invasive diagnostic methods for urology. The introduction of new diagnostic methods into medicine requires the development of new tools for collecting, processing and analysing the data obtained from them. Such system might be seen as a part of the electronic health record EHR system. The digital medical data management platform must provide the infrastructure that will make medical activity possible and effective in the presented scope. The solution presented in this article was implemented using the newest computer technologies to obtain advantages such as mobility, versatility, flexibility and scalability. The architecture of the developed platform, technological stack proposals, database structure and user interface are presented. In the course of this study, an analysis of known and available standards such as Hl7, RIM, DICOM, and tools for collecting medical data was performed, and the results obtained using them are also presented. The developed digital platform also falls into an innovative path of creating a network of sensors communicating with each other in the digital space, resulting in the implementation of the IoT (Internet of Things) vision. The issues of building software based on the architecture of microservices were discussed emphasizing the role of message brokers. The selected message brokers were also analysed in terms of available features and message transmission time.

Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification.

BackgroundElectronic health records (EHRs) are a rich source of longitudinal patient data. However, missing information due to clinical care that predated the implementation of EHR system(s) or care that occurred at different medical institutions impedes complete ascertainment of a patient’s medical history.ObjectiveThis study aimed to investigate information discrepancies and to quantify information gaps by comparing the gynecological surgical history extracted from an EHR of a single institution by using natural language processing (NLP) techniques with the manually curated surgical history information through chart review of records from multiple independent regional health care institutions.MethodsTo facilitate high-throughput evaluation, we developed a rule-based NLP algorithm to detect gynecological surgery history from the unstructured narrative of the Mayo Clinic EHR. These results were compared to a gold standard cohort of 3870 women with gynecological surgery status adjudicated using the Rochester Epidemiology Project medical records–linkage system. We quantified and characterized the information gaps observed that led to misclassification of the surgical status.ResultsThe NLP algorithm achieved precision of 0.85, recall of 0.82, and F1-score of 0.83 in the test set (n=265) relative to outcomes abstracted from the Mayo EHR. This performance attenuated when directly compared to the gold standard (precision 0.79, recall 0.76, and F1-score 0.76), with the majority of misclassifications being false negatives in nature. We then applied the algorithm to the remaining patients (n=3340) and identified 2 types of information gaps through error analysis. First, 6% (199/3340) of women in this study had no recorded surgery information or partial information in the EHR. Second, 4.3% (144/3340) of women had inconsistent or inaccurate information within the clinical narrative owing to misinterpreted information, erroneous “copy and paste,” or incorrect information provided by patients. Additionally, the NLP algorithm misclassified the surgery status of 3.6% (121/3340) of women.ConclusionsAlthough NLP techniques were able to adequately recreate the gynecologic surgical status from the clinical narrative, missing or inaccurately reported and recorded information resulted in much of the misclassification observed. Therefore, alternative approaches to collect or curate surgical history are needed.

Quantitating and assessing interoperability between electronic health records.

Electronic health records (EHRs) contain a large quantity of machine-readable data. However, institutions choose different EHR vendors, and the same product may be implemented differently at different sites. Our goal was to quantify the interoperability of real-world EHR implementations with respect to clinically relevant structured data. We analyzed de-identified and aggregated data from 68 oncology sites that implemented 1 of 5 EHR vendor products. Using 6 medications and 6 laboratory tests for which well-accepted standards exist, we calculated inter- and intra-EHR vendor interoperability scores. The mean intra-EHR vendor interoperability score was 0.68 as compared to a mean of 0.22 for inter-system interoperability, when weighted by number of systems of each type, and 0.57 and 0.20 when not weighting by number of systems of each type. In contrast to data elements required for successful billing, clinically relevant data elements are rarely standardized, even though applicable standards exist. We chose a representative sample of laboratory tests and medications for oncology practices, but our set of data elements should be seen as an example, rather than a definitive list. We defined and demonstrated a quantitative measure of interoperability between site EHR systems and within/between implemented vendor systems. Two sites that share the same vendor are, on average, more interoperable. However, even for implementation of the same EHR product, interoperability is not guaranteed. Our results can inform institutional EHR selection, analysis, and optimization for interoperability.

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system

The core objective of secret sharing concentrates on developing a novel technique that prevents the destruction and leakage of original data during the distribution and encoding processes. Progressive Visual Cryptography (VC) is considered for the potential over the traditional VC schemes since the former does not require and does not suffer from the limitations of requiring a minimum number of participants during the process of encryption and sharing. The chaotic map-based Progressive VC is superior in facilitating predominant secrecy under sharing and encryption. In this paper, an Improved Henon Chaotic Map-based Progressive Block-based VC (IHCMPBVC) scheme is proposed to prevent the leakage and destruction of sensitive information during an exchange and encryption. This proposed IHCMPBVC technique uses the merits of Henon and Lorentz maps for effective encryption since it introduces the option of deriving non-linear behavior that results in sequence generation that covers the complete range with proper distribution in order to minimize the degree of leaks in sharing. The simulation results of the proposed IHCMPBVC technique investigated using entropy, PSNR, and Mean Square Error were improved at an average rate of 27%, 23%, and 31%, predominant to the baseline VC approaches considered in the comparison.

Online patient portal-based management of medication renewal and refill pickup in ambulatory care settings: A retrospective utilization study at tertiary care hospital in Saudi Arabia

BackgroundThe prescription pickup and renewal process in ambulatory care settings requires numerous steps, such as making an appointment with a physician to renew prescriptions and direct pharmacy visits to pick up medications. This process can be difficult or cumbersome for some patients; however, digital health-associated patient portals can reduce the burden on both patients and healthcare professionals. MethodsA retrospective study was conducted in an ambulatory care setting of Johns Hopkins Aramco Healthcare. We analyzed the utilization pattern of MyChart for medication renewal and refill pickup services for ambulatory care patients of all specialties from October 1, 2018, to September 30, 2020. The data were extracted electronically from the Epic-Hyperspace EHR system, and the effects of factors such as year of access and COVID-19 on MyChart utilization were analyzed. ResultsA total of 125,538 patients were registered using MyChart. In the first and second year of this study, MyChart was utilized by 44,063 (8.7%) and 59,622 (13.6%) patients, respectively, for medication pickup. Additionally, in these two years, 92,997 (21.6%) and 156,020 (38.9%) medication refills were requested through MyChart (with no direct pharmacy visit) and collected from different pickup locations, respectively. In two years, there were 363,159 medications sent to physicians for renewal through Epic-MyChart, of which 347,244 (95.6%) were approved and 15,915 (4.4%) were denied. A significant increasing (p less than 0.05) trend in utilization, medication requests, and renewal requests using the MyChart were observed over a period of 24 months and during quarantine due to COVID-19. Although there was a decrease in physicians denying renewal request, these were not significantly affected by time or COVID-19. ConclusionThe high and consistent utilization of the patient portal MyChart indicates its broad acceptance, significantly minimizing the barriers to medication pickup and renewal processes in ambulatory care settings. The year of access and COVID-19 were significantly associated with an increasing trend in MyChart utilization. With increased utilization and higher acceptability, the internet-based patient portal MyChart continues to hold great potential for providing quality healthcare services by increasing access and making patients decision-makers in their healthcare.