Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews

Implementing Accuracy, Completeness, and Traceability for Data Reliability

Importance

While it is well known that data quality underlies evidence validity, the measurement and impacts of data reliability are less well understood. The need has been highlighted in the 21st Century Cures Act of 2016 and US Food and Drug Administration (FDA) Real-World Evidence Program framework in 2018, draft guidance in 2021 and final guidance in 2024. Timely visibility into implementation may be provided by the Transforming Real-World Evidence With Unstructured and Structured Data to Advance Tailored Therapy (TRUST) study, a Verantos Inc-led FDA-funded demonstration project to explore data quality and inform regulatory decision-making.

Objective

To report early learnings from the TRUST study on distilling data reliability to practice including developing a practical approach to quantify accuracy, completeness, and traceability of real-world data (routinely collected patient health data) and comparing traditional to advanced data and technologies on these dimensions.

Design, Setting, and Participants

This quality improvement study was performed using data from 58 hospitals and more than 1180 associated outpatient clinics from academic and community settings in the US. Participants included patients with asthma treated between January 1, 2014, and December 31, 2022. Data were analyzed from January 1 to June 30, 2024.

Exposures

The traditional approach used medical and pharmacy claims as source documentation. The advanced approach used medical and pharmacy claims, electronic health records with unstructured data extracted using artificial intelligence methods, and mortality registry data.

Main Outcomes and Measures

Accuracy was assessed using the F1 score. Completeness was estimated as a weighted mean of available data sources during each calendar year under study for each patient. Traceability was estimated as the proportion of data elements identified in clinical source documentation.

Results

In total, 120 616 patients met the minimum data requirements (mean [SD] age, 43.2 [18.5] years; 41 011 male [34.0%]). For accuracy, traditional approaches had F1 scores of 59.5% and advanced approaches had scores of 93.4%. For completeness, traditional approaches yielded mean scores of 46.1% (95% CI, 38.2%-54.0%); advanced approaches, 96.6% (95% CI, 85.8%-1.1%). For traceability, traditional approaches had 11.5% (95% CI, 11.4%-11.5%) and advanced approaches had 77.3% (95% CI, 77.3%-77.3%) of data elements traceable to clinical source data.

Conclusions and Relevance

In this study, practical implementation of data reliability measurement is described. Findings suggest the potential of using multiple data sources and applying advanced methods to increase real-world data reliability. The inclusion of data reliability standards when generating evidence from these sources has the potential to strengthen support for the use of real-world evidence in the prescription, reimbursement, and approval of medications.

Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction

BACKGROUND

Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

METHODS

To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.

RESULTS

Taking disease risk prediction as a use case, we demonstrate that explicitly modeling family relations significantly improves predictions across the disease spectrum. We then show how ALIGATEHR's attention mechanism, which links patients' disease risk to their relatives' clinical profiles, successfully captures genetic aspects of diseases using longitudinal EHR diagnosis data. Finally, we use ALIGATEHR to successfully distinguish the 2 main inflammatory bowel disease subtypes with highly shared risk factors and symptoms (Crohn's disease and ulcerative colitis).

CONCLUSION

Overall, our results highlight that family relations should not be overlooked in EHR research and illustrate ALIGATEHR's great potential for enhancing patient representation learning for predictive and interpretable modeling of EHRs.

Development of a code system for allergens and its integration into the HL7 FHIR AllergyIntolerance resource

BACKGROUND

Allergy code systems are essential for safety and medical information interoperability. However, current terminology systems lack allergens unique to Japan.

OBJECTIVE

This study established a code system encompassing Japanese food and non-food/non-medication allergens (JFAGY), and developed a meta-code system for integration with existing drug code systems. The practicality and limitations of the JFAGY were assessed by profiling HL7 FHIR allergy intolerance.

METHODS

Allergen terms were selected based on the Standard Commodity Classification of Japan. Additional terms were extracted from clinical guidelines and public documents. For non-food, non-medication allergens, terms from the clinical guidelines were manually compiled to conform to a classification hierarchy. To validate the coverage of the developed food allergen code system, we extracted 823 unique food allergens, totaling 12,027 entries, from two years of electronic health records (EHRs) and performed manual mapping to the code system.

RESULTS

In total, 1,123 food and 607 non-food/non-medication allergen terms were included. The three-digit meta-code system comprises an identifier for coding systems, code length, and allergen categories. The codes allowed the determination of hierarchical relationships between any two terms. The Japanese allergy intolerance value set was developed and bound to the allergy intolerance code. Of the food allergens extracted from EHRs, 62.9% corresponded to unique codes, 6.1% to multiple codes, and 31.0% were unmapped, accounting for 91.5%, 1.9%, and 6.6% of entries, respectively.

CONCLUSIONS

The JFAGY encompasses Japanese-specific food and non-food/non-medication allergens, enabling hierarchy determination between two terms, and playing a critical role in medical safety. When utilizing the JFAGY with the FHIR allergy intolerance resource, an FHIR extension must be included to denote a denied allergy.

Electronic health records and data exchange in the WHO European region: A subregional analysis of achievements, challenges, and prospects

Electronic health record (EHR) systems are powerful tools that enhance healthcare quality. They improve efficiency, enable data exchange, and ensure authorized access to patient information. In 2022, the World Health Organization Regional Office for Europe (WHO EURO) conducted a survey to assess the digital health capabilities of the 53 Member States. This article provides a sub-regional analysis of the status of EHR systems and major barriers to their implementation, their readiness for information sharing, and the access and re-use of EHR data. Generally, EHR implementation and national data exchange are at an advanced stage in the region, though achievements and challenges vary across subregions. While more Member States in the Eastern, Western, and Southern Europe subregions reported having centralized national EHR systems, the situation is more diverse in Northern Europe and the Asian subregions, where both centralized and decentralized EHR systems are in use. Significant barriers to EHR implementation, including funding, technical capacity, competing priorities, and lack of interoperability standards are frequently cited, while others like demand, knowledge or acceptance challenges are not reported as significant. Significant barriers were reported the most by the Central Asia subregion, while barriers had least significance in Western Europe. Five out of the six subregions reported a wide adoption of national strategies and have dedicated agencies to ensure interoperability and secure data exchange. However, only 29 Member States have established legal requirements for healthcare providers to adopt EHR systems that conform to national standards for both clinical terminology and electronic messaging, with this being most notable in Western, Eastern, and Northern Europe, and the lowest percentage of Member States in Central Asia. All Member States of the six sub regions have passed privacy and data protection legislation. The use of EHR data is widely regulated, with only five remaining Member States of WHO Europe to develop EHR legislation distributed across subregions (Southern Europe, Western and Central Asia). Looking ahead, Member States are encouraged to define national legislation governing EHR systems and their use, while ensuring the interconnectivity of the local and regional EHR systems. Sustainable funding should be allocated to the development and maintenance of these systems. Efforts should also focus on creating comprehensive roadmaps for the full implementation of health data standards, addressing interoperability at local and regional levels, and developing quality management systems for testing and certification. Additionally, monitoring and evaluation should be conducted to assess whether EHRs are contributing to national health objectives. Finally, engaging patients and intersectoral partners will be key to developing a more patient-centered approach, ensuring that EHR systems meet patient needs and expectations.

Healthcare professionals' cross-organizational access to electronic health records: A scoping review

BACKGROUND

Cross-organizational access to shared electronic health records can enhance integrated, people-centered health services. However, a gap remains between these potential benefits and the limited support currently offered by electronic health records. The Valkyrie research project aims to bridge this gap by developing a technical prototype of an architecture to promote healthcare service coordination.

OBJECTIVE

To inform the Valkyrie project, we aimed to evaluate approaches for healthcare professionals' access to electronic health records across healthcare providers and identify factors influencing the success and failure of these approaches.

MATERIALS AND METHODS

Using the Joanna Briggs Institute guidance for scoping reviews, searches were conducted in six research databases and grey literature, without limitations on year or language. Papers selected for full-text review were analyzed, and data was extracted using standardized forms that reflected the population, concept, and context framework and the categorization model used in the qualitative analysis of the barriers and facilitators reported in the included papers.

RESULTS

Among the 290 identified papers, five were deemed eligible for full-text review. The included papers were heterogeneous in country, year of publication, study setting, implementation level, and access approaches to electronic health records, highlighting various techniques, from federated to centralized, for accessing shared electronic health records.

DISCUSSION AND CONCLUSION

The review did not identify one single superior access approach. However, a hybrid approach incorporating components from the different approaches combined with emerging technologies may benefit the Valkyrie project. The key facilitators were identified as improved information quality and flexible and easy access. In contrast, lack of trust and poor information quality were significant barriers to successful cross-organizational access to electronic health records. Future research should explore alternative access approaches, considering information quality, user training, and collegial trust across healthcare providers.

Advanced Approaches to Generating High-validity Real-world Evidence in Asthma

BACKGROUND

Asthma is a phenotypically complex disease requiring nuanced data to generate clinically and scientifically robust real-world evidence. A quantitative measure of data quality is important for variables key to the research questions at hand. Using electronic health record (EHR) data, this study compared accuracy for asthma features between traditional real-world evidence approaches using structured data and advanced approaches applying artificial intelligence technologies to unstructured clinical data.

METHODS

We extracted 18 protocol-defined features from 6037 healthcare encounters among 3481 patients. Features included asthma severity subtypes, comorbidities, symptoms, findings, and procedures. We created a manual reference standard through chart abstraction, with two annotators reviewing each record. We assessed interrater reliability using Cohen's kappa score and accuracy against the reference standard as an F1-score.

RESULTS

In the traditional study arm, average recall was 40.8%, precision 72.5%, and F1-score across features was 52.2%. In the advanced study arm, average recall was 95.7%, precision 93.8%, and F1-score was 94.7%. There was an absolute increase of 42.5% and a relative increase of 81.4% in the F1-score between traditional and advanced approaches. Cohen's kappa score indicated 0.80 inter-rater reliability, reflecting a credible reference standard.

CONCLUSIONS

Use of advanced approaches can enable high-quality real-world data sets in asthma, including granular clinical features such as disease subtypes and symptomatic outcomes. Data quality can be measured and, when high, can support generation of high-validity real-world evidence using routinely collected healthcare data.

Assessing the factors militating against the effective implementation of electronic health records (EHR) in Nigeria

This study assessed the factors militating against the effective implementation of electronic health records (EHR) in Nigeria, the computerization of patients' health records with a lot of benefits including improved patients' satisfaction, improved care processes, reduction of patients' waiting time, and medication errors. Despite these benefits, healthcare organizations are slow to adopt the EHR system. Therefore, the study assessed the factors militating against the effective implementation of the EHR system, the level of awareness of EHR, and the utilization of electronic health records; it also investigated the factors militating against the effective implementation of EHR. This is a descriptive cross-sectional study conducted among members of staff of the University of Medical Sciences Teaching Hospital (UNIMEDTH). Purposive sampling was adopted to select the study participants, and a structured questionnaire was used for data collection. Statistical Product and Service Solutions (SPSS) version 27 was used for data analysis, R and Microsoft Excel were used for data visualization. Findings revealed that the respondents had a high level of awareness of EHR with an average mean of 1.29. The study revealed a low utilization rate for EHR (mean 3.01, SD 0.115) and data storage (mean 3.00, SD 0.231), indicating major problems with EHR implementation. Financial limitations (mean 2.63, SD 0.486) and insufficient ICT resources (mean 2.62, SD 0.516) are the main challenges. The regression analysis revealed that EHR utilization is highly impacted by awareness (F = 4.26, p < 0.008), accounting for 15.1% of the variance. Governmental assistance and financial constraints showed a statistically significant negative correlation (r=-0.04, p < 0.05), while inadequate internet connectivity and epileptic power supply showed a statistically significant positive association (r = 0.1, p < 0.05). The results show that, more government support is needed to alleviate financial constraints, and a stable power supply may enhance internet accessibility. The study concluded that epileptic power supply, poor internet connectivity, financial constraints, and other factors mentioned in the study militate against the effective implementation of electronic health records. It was recommended that the management should implement electronic health records effectively for efficient and effective health care delivery.

Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients

Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various applications can optimize the care of peripheral artery disease (PAD) patients in diagnosis, predicting patient outcomes, and imaging interpretation. In this review, we introduce various AI applications such as natural language processing, supervised machine learning, and deep learning, and we analyze the current literature in which these algorithms have been applied to PAD.

Evaluating the Impact of the World Health Organization's Surgical Safety Checklist on Clinical Outcomes and Implementation Strategies: A Systematic Review

This systematic review evaluates the impact and implementation strategies of the World Health Organization's Surgical Safety Checklist (WHO SSC) across diverse healthcare settings since its introduction in 2008. Our comprehensive analysis synthesizes findings from various study designs, including randomized controlled trials, qualitative studies, and meta-analyses, focusing on the checklist's effectiveness in reducing surgical complications and enhancing safety cultures within surgical teams. Despite its widespread endorsement and documented benefits, the review highlights significant variability in implementation quality and adherence, influenced by cultural, institutional, and procedural factors. The findings suggest that optimized adaptation and contextual application of the WHO SSC are crucial for maximizing its clinical benefits, particularly in low-resource settings. This review not only confirms the checklist's efficacy in improving surgical outcomes but also underscores the need for evidence-based strategies to enhance its global implementation and effectiveness.

Exploring potential drivers of patient engagement with their health data through digital platforms: A scoping review

BACKGROUND

Patient engagement when providing patient access to health data results from an interaction between the available tools and individual capabilities. The recent digital advancements of the healthcare field have altered the manifestation and importance of patient engagement. However, a comprehensive assessment of what factors contribute to patient engagement remain absent. In this review article, we synthesised the most frequently discussed factors that can foster patient engagement with their health data.

METHODS

A scoping review was conducted in MEDLINE, Embase, and Google Scholar. Relevant data were synthesized within 7 layers using a thematic analysis: (1) social and demographic factors, (2) patient ability factors, (3) patient motivation factors, (4) factors related to healthcare professionals' attitudes and skills, (5) health system factors, (6) technological factors, and (7) policy factors.

RESULTS

We identified 5801 academic and 200 Gy literature records, and included 292 (4.83%) in this review. Overall, 44 factors that can affect patient engagement with their health data were extracted. We extracted 6 social and demographic factors, 6 patient ability factors, 12 patient motivation factors, 7 factors related to healthcare professionals' attitudes and skills, 4 health system factors, 6 technological factors, and 3 policy factors.

CONCLUSIONS

Improving patient engagement with their health data enables the development of patient-centered healthcare, though it can also exacerbate existing inequities. While expanding patient access to health data is an important step towards fostering shared decision-making in healthcare and subsequently empowering patients, it is important to ensure that these developments reach all sectors of the community.

Optimizing Pain Management in Emergency Departments: A Comprehensive Review of Current Analgesic Practices

Effective pain management in Emergency Departments (EDs) is vital for improving patient comfort and clinical outcomes. This review provides a comprehensive analysis of current pain management practices in ED settings, focusing on the challenges and opportunities for optimization. The review examines pharmacologic and non-pharmacologic pain management strategies, evaluating their effectiveness and identifying inconsistencies and gaps in current practices. Key challenges in the ED environment include time constraints, variability in clinical protocols, and the need to address diverse patient needs, including those of paediatric, geriatric, and chronic pain patients. The review highlights the importance of standardized pain assessment tools and protocols to improve consistency in pain management. Innovations, such as technological advances and multimodal approaches, are explored for their potential to enhance pain management practices. Recommendations address identified challenges, including improved training for ED staff, the development of evidence-based protocols, and the integration of multimodal pain management strategies. By addressing these areas, the review aims to contribute to the development of more effective and uniform pain management practices in emergency care, ultimately leading to better patient outcomes and experiences. This review emphasizes the need for ongoing research and adaptation of best practices to meet the evolving needs of patients in emergency settings.

Balancing confidentiality and care coordination: challenges in patient privacy

BACKGROUND

In the digital age, maintaining patient confidentiality while ensuring effective care coordination poses significant challenges for healthcare providers, particularly nurses.

AIM

To investigate the challenges and strategies associated with balancing patient confidentiality and effective care coordination in the digital age.

METHODS

A cross-sectional study was conducted in a general hospital in Egypt to collect data from 150 nurses across various departments with at least six months of experience in patient care. Data were collected using six tools: Demographic Form, HIPAA Compliance Checklist, Privacy Impact Assessment (PIA) Tool, Data Sharing Agreement (DSA) Framework, EHR Privacy and Security Assessment Tool, and NIST Cybersecurity Framework. Validity and Reliability were ensured through pilot testing and factor analysis.

RESULTS

Participants were primarily aged 31-40 years (45%), with 75% female and 60% staff nurses. High compliance was observed in the HIPAA Compliance Checklist, especially in Administrative Safeguards (3.8 ± 0.5), indicating strong management and training processes, with an overall score of 85 ± 10. The PIA Tool showed robust privacy management, with Project Descriptions scoring 4.5 ± 0.3 and a total score of 30 ± 3. The DSA Framework had a mean total score of 20 ± 2, with Data Protection Measures scoring highest at 4.0 ± 0.4. The EHR assessments revealed high scores in Access Controls (4.4 ± 0.3) and Data Integrity Measures (4.3 ± 0.3), with an overall score of 22 ± 1.5. The NIST Cybersecurity Framework had a total score of 18 ± 2, with the highest scores in Protect (3.8) and lower in Detect (3.6). Strong positive correlations were found between HIPAA Compliance and EHR Privacy (r = 0.70, p < 0.05) and NIST Cybersecurity (r = 0.55, p < 0.05), reflecting effective data protection practices.

CONCLUSION

The study suggests that continuous improvement in privacy practices among healthcare providers, through ongoing training and comprehensive privacy frameworks, is vital for enhancing patient confidentiality and supporting effective care coordination.

Electronic health record modification and dashboard development to improve clinical care in pediatric rheumatology

Objective

This report describes our experience in electronic health record (EHR) note modification and creation of an external dashboard to create a local learning health system that contributes to quality improvement and patient care within our pediatric rheumatology clinic.

Methods

We applied quality improvement methodology to develop a more reliable and accurate system to identify patients with juvenile idiopathic arthritis and track important measures that aide in improving patient care and performance outcomes. From 2019 to 2021, we iteratively modified our outpatient clinic EHR note to include structured data elements to improve longitudinal monitoring. We then validated data transferred to an electronic dashboard external to the EHR and demonstrated utility for identifying an accurate patient population and tracking quality improvement initiatives.

Results

Creation of the structured data elements improved the identification of patients with JIA with >99% accuracy and without requiring manual review of the chart. Using the dashboard to monitor performance, we improved documentation of critical disease activity measures that resulted in improvement in those scores across the local population of patients with JIA. The structured data elements also enabled us to automate electronic data transfer to a multicenter learning network registry.

Conclusion

The structured data element modifications made to our outpatient EHR note populate a local dashboard that allows real time access to critical information for patient care, population management, and improvement in quality metrics. The collection and monitoring of structured data can be scaled to other quality improvement initiatives in our clinic and shared with other centers.

The effectiveness of checklists and error reporting systems in enhancing patient safety and reducing medical errors in hospital settings: A narrative review

Objectives

This narrative review aimed to explore the impact of checklists and error reporting systems on hospital patient safety and medical errors.

Methods

A systematic search of academic databases from 2013 to 2023 was conducted, and peer-reviewed studies meeting inclusion criteria were assessed for methodological rigor. The review highlights evidence supporting the efficacy of checklists in reducing medication errors, surgical complications, and other adverse events. Error reporting systems foster transparency, encouraging professionals to report incidents and identify systemic vulnerabilities.

Results

Checklists and error reporting systems are interconnected. Interprofessional collaboration is emphasized in checklist implementation. In this review, limitations arise due to the different methodologies used in the articles and potential publication bias. In addition, language restrictions may exclude valuable non-English research. While positive impacts are evident, success depends on organizational culture and resources.

Conclusions

This review contributes to patient safety knowledge by examining the relevant literature, emphasizing the importance of interventions, and calling for further research into their effectiveness across diverse healthcare and cultural settings. Understanding these dynamics is crucial for healthcare providers to optimize patient safety outcomes.

Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential applications in the medical field are extensive and vary from extracting data from Electronic Health Records -one of its most well-known and frequently exploited uses- to investigating relationships among genetics, biomarkers, drugs, and diseases for the proposal of new medications. NLP can be useful for clinical decision support, patient monitoring, or medical image analysis. Despite its vast potential, the real-world application of NLP is still limited due to various challenges and constraints, meaning that its evolution predominantly continues within the research domain. However, with the increasingly widespread use of NLP, particularly with the availability of large language models, such as ChatGPT, it is crucial for medical professionals to be aware of the status, uses, and limitations of these technologies.

Exploring the Impact of In Basket Metrics on the Adoption of a New Electronic Health Record System Among Specialists in a Tertiary Hospital in Alberta: Descriptive Study

BACKGROUND

Health care organizations implement electronic health record (EHR) systems with the expectation of improved patient care and enhanced provider performance. However, while these technologies hold the potential to create improved care and system efficiencies, they can also lead to unintended negative consequences, such as patient safety issues, communication problems, and provider burnout.

OBJECTIVE

This study aims to document metrics related to the In Basket communication hub (time in In Basket per day, time in In Basket per appointment, In Basket messages received per day, and turnaround time) of the EHR system implemented by Alberta Health Services, the province-wide health delivery system called Connect Care (Epic Systems). The objective was to identify how a newly implemented EHR system was used, the timing of its use, and the duration of use specifically related to In Basket activities.

METHODS

A descriptive study was conducted. Due to the diversity of specialties, the providers were grouped into medical and surgical based on previous similar studies. The participants were further subgrouped based on their self-reported clinical full-time equivalent (FTE ) measure. This resulted in 3 subgroups for analysis: medical FTE <0.5, medical FTE >0.5, and surgical (all of whom reported FTE >0.5). The analysis was limited to outpatient clinical interactions and explicitly excluded inpatient activities.

RESULTS

A total of 72 participants from 19 different specialties enrolled in this study. The providers had, on average, 8.31 appointments per day during the reporting periods. The providers received, on average, 21.93 messages per day, and they spent 7.61 minutes on average in the time in In Basket per day metric and 1.84 minutes on average in the time in In Basket per appointment metric. The time for the providers to mark messages as done (turnaround time) was on average 11.45 days during the reporting period. Although the surgical group had, on average, approximately twice as many appointments per scheduled day, they spent considerably less connected time (based on almost all time metrics) than the medical group. However, the surgical group took much longer than the medical group to mark messages as done (turnaround time).

CONCLUSIONS

We observed a range of patterns with no consistent direction. There does not seem to be evidence of a "learning curve," which would have shown a consistent reduction in time spent on the system over time due to familiarity and experience. While this study does not show how the included metrics could be used as predictors of providers' satisfaction or feelings of burnout, the use trends could be used to start discussions about future Canadian studies needed in this area.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

Enhancing the Efficiency of a Radiation Oncology Department Using Electronic Medical Records: Protocol for Preparing Radiotherapy

BACKGROUND

Electronic medical records (EMRs) streamline medical processes, improve quality control, and facilitate data sharing among hospital departments. They also reduce maintenance costs and storage space needed for paper records, while saving time and providing structured data for future research.

OBJECTIVE

This study aimed to investigate whether the integration of the radiation oncology information system and the hospital information system enhances the efficiency of the department of radiation oncology.

METHODS

We held multidisciplinary discussions among physicians, physicists, medical radiation technologists, nurses, and engineers. We integrated paper records from the radiation oncology department into the existing hospital information system within the hospital. A new electronic interface was designed. A comparison was made between the time taken to retrieve information from either the paper records or the EMRs for radiation preparation. A total of 30 cases were randomly allocated in both the old paper-based system and the new EMR system. The time spent was calculated manually at every step during the process, and we performed an independent 1-tailed t test to evaluate the difference between the 2 systems.

RESULTS

Since the system was launched in August 2020, more than 1000 medical records have been entered into the system, and this figure continues to increase. The total time needed for the radiation preparation process was reduced from 286.8 minutes to 154.3 minutes (P<.001)-a reduction of 46.2%. There was no longer any need to arrange for a nurse to organize the radiotherapy paper records, saving a workload of 16 hours per month.

CONCLUSIONS

The implementation of the integrated EMR system has resulted in a significant reduction in the number of steps involved in radiotherapy preparation, as well as a decrease in the amount of time required for the process. The new EMR system has provided numerous benefits for the department, including a decrease in workload, a simplified workflow, and conserving more patient data within a confined space.

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Introduction

Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention.

Objective

This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS.

Method

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery.

Results

This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed.

Discussions

Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements.

Conclusion

The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records

Background

Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators.

Setting

EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs.

Objectives

To determine whether the time window proximal to a hospitalised suicide attempt can be discriminated from a distal period of lower risk by analysing the documentation and mental health clinical free text data from EHRs and (i) investigate whether the rate at which EHR documents are recorded per patient is associated with a suicide attempt; (ii) compare document-level word usage between documents proximal and distal to a suicide attempt; and (iii) compare n-gram frequency related to third-person pronoun use proximal and distal to a suicide attempt using machine learning.

Methods

The Clinical Record Interactive Search (CRIS) system allowed access to de-identified information from the EHRs. CRIS has been linked with Hospital Episode Statistics (HES) data for Admitted Patient Care. We analysed document and event data for patients who had at some point between 1 April 2006 and 31 March 2013 been hospitalised with a HES ICD-10 code related to attempted suicide (X60-X84; Y10-Y34; Y87.0/Y87.2).

Findings

n = 8,247 patients were identified to have made a hospitalised suicide attempt. Of these, n = 3,167 (39.8%) of patients had at least one document available in their EHR prior to their first suicide attempt. N = 1,424 (45.0%) of these patients had been "monitored" by mental healthcare services in the past 30 days. From 60 days prior to a first suicide attempt, there was a rapid increase in the monitoring level (document recording of the past 30 days) increasing from 35.1 to 45.0%. Documents containing words related to prescribed medications/drugs/overdose/poisoning/addiction had the highest odds of being a risk indicator used proximal to a suicide attempt (OR 1.88; precision 0.91 and recall 0.93), and documents with words citing a care plan were associated with the lowest risk for a suicide attempt (OR 0.22; precision 1.00 and recall 1.00). Function words, word sequence, and pronouns were most common in all three representations (uni-, bi-, and tri-gram).

Conclusion

EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point.

21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework

In this paper, we present a framework for developing a Learning Health System (LHS) to provide means to a computerized clinical decision support system for allied healthcare and/or nursing professionals. LHSs are well suited to transform healthcare systems in a mission-oriented approach, and is being adopted by an increasing number of countries. Our theoretical framework provides a blueprint for organizing such a transformation with help of evidence based state of the art methodologies and techniques to eventually optimize personalized health and healthcare. Learning via health information technologies using LHS enables users to learn both individually and collectively, and independent of their location. These developments demand healthcare innovations beyond a disease focused orientation since clinical decision making in allied healthcare and nursing is mainly based on aspects of individuals' functioning, wellbeing and (dis)abilities. Developing LHSs depends heavily on intertwined social and technological innovation, and research and development. Crucial factors may be the transformation of the Internet of Things into the Internet of FAIR data & services. However, Electronic Health Record (EHR) data is in up to 80% unstructured including free text narratives and stored in various inaccessible data warehouses. Enabling the use of data as a driver for learning is challenged by interoperability and reusability.To address technical needs, key enabling technologies are suitable to convert relevant health data into machine actionable data and to develop algorithms for computerized decision support. To enable data conversions, existing classification and terminology systems serve as definition providers for natural language processing through (un)supervised learning.To facilitate clinical reasoning and personalized healthcare using LHSs, the development of personomics and functionomics are useful in allied healthcare and nursing. Developing these omics will be determined via text and data mining. This will focus on the relationships between social, psychological, cultural, behavioral and economic determinants, and human functioning.Furthermore, multiparty collaboration is crucial to develop LHSs, and man-machine interaction studies are required to develop a functional design and prototype. During development, validation and maintenance of the LHS continuous attention for challenges like data-drift, ethical, technical and practical implementation difficulties is required.

The unfulfilled promises of electronic health records

We provide a brief update on the current evidence on electronic health records' benefits, risks, and potential harms through a rapid narrative review. Many of the promised benefits of electronic health records have not yet been realised. Electronic health records are often not user-friendly. To enhance their potential, electronic health record platforms should be continuously evaluated and enhanced by carefully considering feedback from all stakeholders.

Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review

Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical trials that do not rely on "real world evidence" such as contained in electronic health records (EHR). Consequently, we can expect an increase in the number of reports of these types of trials, which we identify here as 'EHR-sourced trials.' 'In this selected literature review, we discuss the various designs and the ethical issues they raise. EHR-sourced trials have the potential to improve/increase common data elements and other aspects of the EHR and related systems. Caution is advised, however, in drawing causal inferences about the relationships among EHR variables. Nevertheless, we anticipate that EHR-CTs will play a central role in answering research and regulatory questions.

Ten Topics to Get Started in Medical Informatics Research

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

Clinical Informatics and the Electronic Medical Record

The electronic medical record has fundamentally altered the way surgeons participate and practice medicine. There is now a wealth of data, once hidden behind paper records, that is, now available to surgeons to provide superior care to their patients. This article reviews the history of the electronic medical record, discusses use cases of additional data resources, and highlights the pitfalls of this relatively new technology.

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

Applications of artificial intelligence for patients with peripheral artery disease

OBJECTIVE

Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field.

METHODS

We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML).

RESULTS

NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow.

CONCLUSIONS

AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.

Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication.

Investigation and Countermeasures Research of Hospital Information Construction of Tertiary Class-A Public Hospitals in China: Questionnaire Study

BACKGROUND

Medical informatization has initially demonstrated its advantages in improving the medical service industry. Over the past decade, the Chinese government have made a lot of effort to complete infrastructural information construction in the medical and health domain, and smart hospitals will be the next priority according to policies released by Chinese government in recent years.

OBJECTIVE

To provide strategic support for further development of medical information construction in China, this study aimed to investigate the current situation of medical information construction in tertiary class-A public hospitals and analyze the existing problems and countermeasures.

METHODS

This study surveyed 23 tertiary class-A public hospitals in China who voluntarily responded to a self-designed questionnaire distributed in April 2020 to investigate the current medical information construction status. Descriptive statistics were used to summarize the current configurations of hospital information department, hospital information systems, hospital internet service and its application, and the satisfaction of hospital information construction. Interviews were also conducted with the respondents in this study for requirement analysis.

RESULTS

The results show that hospital information construction has become one of the priorities of the hospitals' daily work, and the medical information infrastructural construction and internet service application of the hospitals are good; however, a remarkable gap among the different level of hospitals can be observed. Although most hospitals had built their own IT team to undertake information construction work, the actual utilization rate of big data collected and stored in the hospital information system was not satisfactory.

CONCLUSIONS

Support for the construction of information technology in primary care institutions should be increased to balance the level of development of medical informatization in medical institutions at all levels. The training of complex talents with both IT and medical backgrounds should be emphasized, and specialized disease information standards should be developed to lay a solid data foundation for data utilization and improve the utilization of medical big data.

A blockchain-based computerized network infrastructure for the transparent, immutable calculation and dissemination of quantitative, measurable parameters of academic and medical research publications

Digital transformation of healthcare systems should rely on decentralized computer networks and take advantage of the unique characteristics of blockchain technology. Decentralization ensures process transparency and data transparency for all relevant stakeholders. These values are essential in the realms of populations' healthcare information communications and processing, control and tracking of medical logistics supply chains, clinical research management, and control of certified healthcare services organizations. Mounting decentralized processes onto a blockchain-based computerized network will endow the values of immutability, improved cybersecurity, and potential for incentivizing stakeholders for relevant, pre-determined activities. One of the most relevant processes that would benefit from a decentralized, blockchain-based architecture is the submission, review, and publishing of scientific manuscripts. Current structures and processes in this world are non-transparent, poorly incentivizing significant stakeholders such as manuscripts' reviewers, and many are potentially corrupted. In this review, we suggest a blockchain-based architecture for such systems and advocate further research and development in several domains of modern healthcare systems-offering medicine to become "the new guy on the block (chain)."

Health Information System and Health Care Applications Performance in the Healthcare Arena: A Bibliometric Analysis

There have been several studies centred on health information systems with many insights provided to enhance health care applications globally. These studies have provided theoretical schemes for fortifying the enactment and utilisation of the Health Information System (HIS). In addition, these research studies contribute greatly to the development of HIS in alignment with major stakeholders such as health practitioners and recipients of health care. Conversely, there has been trepidation about HIS' sustainability and resilience for healthcare applications in the era of digitalization and globalization. Hence, this paper investigates research on HIS with a primary focus on health care applications to ascertain its sustainability and resilience amidst the transformation of the global healthcare space. Therefore, using a bibliometric approach, this paper measures the performance of health information systems and healthcare for health care applications using bibliometric data from the web of science database. The findings reveal solid evidence of the constructive transformation of health information systems and health care applications in the healthcare arena, providing ample evidence of the adaptation of HIS and health care applications within the healthcare arena to the fourth industrial revolution and, additionally, revealing the resilient alignment of health care applications and health information systems.

Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study

Background

Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with cancer patients. This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer.

Methods

Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to determine what acceptance factors they find most important for a virtual assistant and gain insights into value-adding applications. The unified theory of acceptance and use of technology (UTAUT) was used to structure perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared.

Results

The interviews found support for all factors of the UTAUT: performance expectancy, effort expectancy, social influence and facilitating conditions. Additionally, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Former patients found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß = 0.399), effort expectancy (ß = 0.258), social influence (ß = 0.114), and trust (ß = 0.210) significantly influenced behavioral intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß = 0.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention.

Conclusions

Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient’s self-efficacy. Therefore, including patients during the development and introduction of a VA in cancer treatment is important. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the VA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08189-7.

An Interoperable Electronic Health Record System for Clinical Cardiology

Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.

Analysis of critical success factors for blockchain technology implementation in healthcare sector

Purpose

Recently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare.

Design/methodology/approach

The paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings.

Findings

Based on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential.

Originality/value

This study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.

Open-source electronic health record systems: A systematic review of most recent advances

Open-source Electronic Health Records (OS-EHRs) are of pivotal importance in the management, operations, and administration of any healthcare organization. With the advancement of health informatics, researchers and healthcare practitioners have proposed various frameworks to assess the maturation of Open-source EHRs. The significance of OS-EHRs stems from the fact that vendor-based EHR implementations are becoming financially burdensome, with some vendors raking in more than $1 billion with one contract. Contrarily, the adoption of OS-EHRs suffers from a lack of systematic evaluation from the standpoint of a standard reference model. To this end, the Healthcare Information and Management Systems Society (HIMSS) has presented a strategic road map called EMR Adoption and Maturity (EMRAM). The HIMSS-EMRAM model proposes a stage-wise model approach that is globally recognized and can be essentially applied as a benchmark evaluation criteria for open-source EHRs. This paper offers an applied descriptive methodology over the frequently studied open-source EHRs currently operational worldwide or has the potential of adoption in healthcare settings. Besides, we also present profiling (User Support, Developer' Support, Customization Support, Technical details, and Diagnostic help) of studied OS-EHRs from developer's and user's perspectives using updated standard metrics. We carried out multi-aspect objective analysis of studied systems covering EHR functions, software based features and implementation. This review portrays systematic aspects of electronic medical record standards for open-source software implementations. As we observed in the literature, prevalent research and working prototypes lack systematic review of the HIMSS-EMRAM model and do not present evolving software features. Therefore, after the application of our assessment measures, the results obtained indicate that OS-EHRs are yet to acquire standard compliance and implementation. The findings in this paper can be beneficial in the planning and implementation of OS-EHRs projects in the future.

[Electronic medical record and prescription: risks and benefits detected since its implementation. Safe designing, rollout and use]

Las nuevas tecnologías de la información han transformado la manera de prestar la asistencia en los servicios de salud, impregnando casi todos los aspectos de la atención sanitaria. A medida que la complejidad del sistema aumenta, es más difícil trabajar de manera óptima sin la asistencia de las nuevas tecnologías. Su implantación supone un avance, bien por el adelanto que entraña el uso adecuado de cualquier nueva tecnología en el cuidado de la salud, bien por el desarrollo de aplicaciones específicas que mejoran la seguridad de la asistencia. Sin embargo, factores como un diseño incorrecto, implementación y mantenimiento deficientes, capacitación inadecuada, junto al exceso de confianza y dependencia, pueden hacer que las tecnologías comprometan, más que favorecer, la seguridad del paciente.

Este artículo describe los efectos beneficiosos, y los que no lo son tanto, de la introducción en nuestro país de la historia clínica y la receta electrónicas en la calidad y la seguridad de la asistencia sanitaria.

Integrating eHealth within a Transforming Mental Healthcare Setting: A Qualitative Study into Values, Challenges, and Prerequisites

Mental health care is shifting towards more person-centered and community-based health care. Although integrating eHealth within a transforming healthcare setting may help accomplishing the shift, research studying this is lacking. This study aims to improve our understanding of the value of eHealth within a transforming mental healthcare setting and to define the challenges and prerequisites for implementing eHealth in particular within this transforming context. In this article, we present the results of 29 interviews with clients, social network members, and professionals of an ambulatory team in transition within a Dutch mental health care institute. The main finding is that eHealth can support a transforming practice shifting towards more recovery-oriented, person-centered, and community-based service in which shared-decision making is self-evident. The main challenge revealed is how to deal with clients' voices, when professionals see the value of eHealth but clients do not want to start using eHealth. The shift towards client-centered and network-oriented care models and towards blended care models are both high-impact changes in themselves. Acknowledging the complexity of combining these high-impact changes might be the first step towards creating blended client-centered and network-oriented care. Future research should examine whether and how these substantial shifts could be mutually supportive.