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Implementation of digital health technologies has grown rapidly, but many remain limited to pilot studies due to challenges, such as a lack of evidence or barriers to implementation. Overcoming these challenges requires learning from previous implementations and systematically documenting implementation processes to better understand the real-world impact of a technology and identify effective strategies for future implementation. A group of global experts, facilitated by the Geneva Digital Health Hub, developed the Guidelines and Checklist for the Reporting on Digital Health Implementations (iCHECK-DH, pronounced \"I checked\") to improve the completeness of reporting on digital health implementations. A guideline development group was convened to define key considerations and criteria for reporting on digital health implementations. To ensure the practicality and effectiveness of the checklist, it was pilot-tested by applying it to several real-world digital health implementations, and adjustments were made based on the feedback received. The guiding principle for the development of iCHECK-DH was to identify the minimum set of information needed to comprehensively define a digital health implementation, to support the identification of key factors for success and failure, and to enable others to replicate it in different settings. The result was a 20-item checklist with detailed explanations and examples in this paper. The authors anticipate that widespread adoption will standardize the quality of reporting and, indirectly, improve implementation standards and best practices. Guidelines for reporting on digital health implementations are important to ensure the accuracy, completeness, and consistency of reported information. This allows for meaningful comparison and evaluation of results, transparency, and accountability and informs stakeholder decision-making. i-CHECK-DH facilitates standardization of the way information is collected and reported, improving systematic documentation and knowledge transfer that can lead to the development of more effective digital health interventions and better health outcomes.

Background: The high prevalence of trau­ma and its negative impact on health among people living with HIV underscore the need for adopting trauma-informed care (TIC), an evidence-based approach to address trauma and its physical and mental sequelae. However, virtually nothing is known about factors internal and external to the clinical environment that might influence adoption of TIC in HIV primary care clinics.Methods: We conducted a pre-implemen­tation assessment consisting of in-depth interviews with 23 providers, staff, and ad­ministrators at a large urban HIV care center serving an un-/under-insured population in the southern United States. We used the Consolidated Framework for Implementa­tion Research (CFIR) to guide qualitative coding to ascertain factors related to TIC adoption.Results: Inner setting factors perceived as impacting TIC adoption within HIV primary care included relative priority, compatibility, available resources, access to knowledge and information (ie, training), and networks and communications. Relevant outer setting factors included patient needs/resources and cosmopolitanism (ie, connections to external organizations). Overall, the HIV care center exhibited high priority and compatibility for TIC adoption but displayed a need for system strengthening with regard to available resources, training, communica­tions, cosmopolitanism, and patient needs/ resources.Conclusions: Through identification of CFIR inner and outer setting factors that might influence adoption of TIC within an HIV primary care clinic, our findings begin to fill key knowledge gaps in understand­ing barriers and facilitators for adopting TIC in HIV primary care settings and highlight implementation strategies that could be employed to support successful TIC imple­mentation. Ethn Dis. 2021;31(1):109-118; doi:10.18865/ed.31.1.109

Objective: To understand barriers and facilitators to the adaptation of programs reflecting changing scientific guidelines for breast/cervical cancer screening, including factors influencing the de-implementation of messaging, program components, or screen­ing practices no longer recommended due to new scientific evidence.Design and Methods: We conducted a con­vergent mixed-methods design in partnership with The National Witness Project (NWP), a nationally implemented evidence-based lay health advisor (LHA) program for breast/cer­vical cancer screening among African Ameri­can (AA) women. Surveys were conducted among 201 project directors (PDs) and LHAs representing 14 NWP sites; in-depth interviews were conducted among 14 PDs to provide context to findings. Survey data and qualitative interviews were collected concur­rently from January 2019-January 2020.Setting: National sample of NWP sites from across the United States.Results: Trust and mistrust were important themes that arose in quantitative and qualita­tive data. Common concerns about adapting to new guidelines included: 1) percep­tions that new guidelines misalign with the personal values and beliefs of AA women; 2) mistrust of guidelines, providers, medical organizations; 3) confusion about inconsis­tent guidelines and concern they are based on studies that don’t reflect the experience of AA women (who experience more aggressive tumors at younger ages); and 4) belief that breast self-exam (BSE) is an empowerment tool for AA women and should be included to promote awareness, given many women discovered lumps/cancer through BSE.Conclusion: Findings highlight that trust and mistrust are important but understudied social determinants of health among AAs that should be considered in implementation science as they: 1) have critical implications for shaping health inequities; and 2) help ex­plain and contextualize why new screening guidelines may not be fully embraced in the AA community.Ethn Dis. 2021;31(1):119- 132; doi:10.18865/ed.31.1.119

Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools produce mixed results and are often disliked by clinicians. Principles from behavioral economics such as \"nudges\" may improve the effectiveness and clinician satisfaction of CDS tools. This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools. The purpose of this paper is to describe a systematic and pragmatic approach grounded in implementation science to identify and prioritize how best to incorporate different types of nudges into CDS tools. We provide a case example of how this systematic approach was applied to design a CDS tool to improve guideline-concordant prescribing of mineralocorticoid receptor antagonists for patients with heart failure and reduced ejection fraction. We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, and Ego nudge framework and the Practical, Robust Implementation and Sustainability Model implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. To illustrate how these frameworks can be applied in a real-life scenario, we use a case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools. We defined four iterative steps guided by the Practical, Robust Implementation and Sustainability Model: (1) engage partners for user-centered design, (2) develop a shared understanding of the nudge types, (3) determine the overarching CDS format, and (4) brainstorm and prioritize nudge types to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned. We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues when designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. This approach can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.

Malignant hyperthermia (MH) seriously threatens perioperative safety. Historically, limited awareness of MH among anesthesiologists and the unavailability of dantrolene have caused a high mortality rate of MH events in China. Although domestic dantrolene has been available in China since 2020, Chinese anesthesiologists continue to face significant challenges in managing MH crises. A WeChat applet-based National Remote Emergency System for Malignant Hyperthermia (MH-NRES) was developed to assist anesthesiologists in making rapid diagnosis, initiating dantrolene mobilization, implementing effective treatment, and subsequently constructing an MH database. However, the effectiveness of MH-NRES in real-world patients experiencing MH in China remains uncertain. This study aimed to assess the effectiveness of MH-NRES in enhancing outcomes for patients with MH. A retrospective pre-post implementation study was conducted from January 2018 to November 2024. The MH-NRES intervention was initiated in December 2022. Medical records were reviewed both before and after the implementation of our intervention, encompassing demographic characteristics, anesthesia-related data, treatment details, and clinical outcomes. Descriptive analyses and a pre-post intervention comparison were used to assess the effectiveness of the MH-NRES intervention. The primary outcome was the mortality of patients with MH. The use of dantrolene and the time interval from the MH episode to the administration of dantrolene were considered secondary outcomes. The user activity metrics of MH-NRES were also reported. After the MH-NRES was launched for public use, the cumulative number of users reached 21,835, with a maximum daily user growth of 689 (median 15, IQR 9-25). The cumulative page views amounted to 245,740 and the average daily page views were 262.8. A total of 34 patients with MH and 14 patients with MH were retrospectively collected before and after the intervention, respectively. The mortality of patients with MH in the postimplementation group was significantly lower compared with that in the preimplementation group (1/14, 7.1% vs 19/34, 55.9%; P=.002). No significant differences were observed in the early clinical manifestations of MH between the 2 groups. The rate of dantrolene use in the postimplementation group was significantly higher than that in the preimplementation group (11/14, 78.6% vs 15/34, 44.1%; P=.03). The dantrolene available time in the postimplementation group was 126.5 minutes earlier than that in the preimplementation group, but the difference did not reach statistical significance (median 113.5, IQR 54.5-244.3 vs 240, IQR 105-324 minutes, P=.08). The MH-NRES aids in improving the timely administration of dantrolene and decreasing mortality rates among patients with MH. This system represents a rare disease perioperative management model and constitutes a specialized perioperative management approach for rare diseases that suits the current medical situation in China.

Frequent vital sign (VS) monitoring is central to inpatient safety but is traditionally performed manually every 4 hours, a century-old practice that can miss early clinical deterioration, disrupt patient sleep, and impose a heavy documentation burden on nursing staff. Continuous VS monitoring (CVSM) using wearable remote patient monitoring devices enables near real-time, high-frequency VS measurement while reducing manual workload and preserving patient rest. This implementation report describes the large-scale implementation of CVSM across an 8-hospital health system. The initiative aimed to (1) enhance earlier detection of patient health deterioration through continuous, algorithm-driven monitoring; (2) improve nursing workflow efficiency by reducing reliance on manual VS checks; and (3) minimize nighttime disruptions to support patient rest and recovery. The program was designed for system-wide scalability and executed from 2022 to 2024 using a 4-phase framework: strategic program design, program planning, go-live preparation, and implementation and optimization. A Food and Drug Administration-cleared wearable device (BioButton) continuously measured heart rate, respiratory rate, and skin temperature, with data integrated into Epic and monitored 24×7 through a centralized virtual operations center. Rollout followed a staggered playbook across approximately 2700 adult non-intensive care unit beds and was supported by leadership engagement, supply chain readiness, staff training, and phased superuser-led adoption. All 8 hospitals achieved full deployment between April 2023 and February 2024, with more than 95% device use rates and 100% nursing staff training completion. A standardized escalation workflow filtered approximately 50% of the alerts at the virtual operations center review stage, substantially reducing frontline alert burden. Operational refinements included revised heart rate and respiratory rate alert thresholds and the removal of temperature as a single alert trigger. Several units extended overnight manual VS intervals from every 4 hours to every 6 to 8 hours, with staff estimating approximately 4 hours saved per nursing shift. Patient care assistants redirected time toward patient mobility and personal care needs, while staff reported growing confidence in device performance over time. This initiative represents the first system-wide deployment of CVSM across a diverse, multihospital health system. Success was enabled by early strategic alignment, phased rollout, robust IT and monitoring infrastructure, and iterative optimization. The program demonstrates the feasibility of embedding CVSM into routine inpatient care to improve efficiency and patient experience. Transferable strategies, including phased rollouts, centralized monitoring, and structured change management, may inform other health systems pursuing digital VS redesign. Future work should rigorously evaluate impacts on patient outcomes, cost-effectiveness, and applicability to postacute and ambulatory care settings.

Artificial intelligence (AI) algorithms offer an effective solution to alleviate the burden of diabetic retinopathy (DR) screening in public health settings. However, there are challenges in translating diagnostic performance and its application when deployed in real-world conditions. This study aimed to assess the technical feasibility of integration and diagnostic performance of validated DR screening (DRS) AI algorithms in real-world outpatient public health settings. Prior to integrating an AI algorithm for DR screening, the study involved several steps: (1) Five AI companies, including four from India and one international company, were invited to evaluate their diagnostic performance using low-cost nonmydriatic fundus cameras in public health settings; (2) The AI algorithms were prospectively validated on fundus images from 250 people with diabetes mellitus, captured by a trained optometrist in public health settings in Chandigarh Tricity in North India. The performance evaluation used diagnostic metrics, including sensitivity, specificity, and accuracy, compared to human grader assessments; (3) The AI algorithm with better diagnostic performance was integrated into a low-cost screening camera deployed at a community health center (CHC) in the Moga district of Punjab, India. For AI algorithm analysis, a trained health system optometrist captured nonmydriatic images of 343 patients. Three web-based AI screening companies agreed to participate, while one declined and one chose to withdraw due to low specificity identified during the interim analysis. The three AI algorithms demonstrated variable diagnostic performance, with sensitivity (60%-80%) and specificity (14%-96%). Upon integration, the better-performing algorithm AI-3 (sensitivity: 68%, specificity: 96, and accuracy: 88·43%) demonstrated high sensitivity of image gradability (99.5%), DR detection (99.6%), and referral DR (79%) at the CHC. This study highlights the importance of systematic AI validation for responsible clinical integration, demonstrating the potential of DRS to improve health care access in resource-limited public health settings.

Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use. In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW. We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights. More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements. Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.

JMIR Medical Informatics is pleased to offer implementation reports as a new article type. Implementation reports present real-world accounts of the implementation of health technologies and clinical interventions. This new article type is intended to promote the rapid documentation and dissemination of the perspectives and experiences of those involved in implementing digital health interventions and assessing the effectiveness of digital health projects.

Background:While high-quality primary health care services can meet 80%-90% of health needs over a person’s lifetime, this potential is severely hindered in many low-resource countries by a constrained health care system. There is a growing consensus that effectively designed, resourced, and managed community health worker programs are a critical component of a well-functioning primary health system, and digital technology is recognized as an important enabler of health systems transformation.Objective:In this implementation report, we describe the design and rollout of Zanzibar’s national, digitally enabled community health program–Jamii ni Afya.Methods:Since 2010, D-tree International has partnered with the Ministry of Health Zanzibar to pilot and generate evidence for a digitally enabled community health program, which was formally adopted and scaled nationally by the government in 2018. Community health workers use a mobile app that guides service delivery and data collection for home-based health services, resulting in comprehensive service delivery, access to real-time data, efficient management of resources, and continuous quality improvement.Results:The Zanzibar government has documented increases in the delivery of health facilities among pregnant women and reductions in stunting among children younger than 5 years since the community health program has scaled. Key success factors included starting with the health challenge and local context rather than the technology, usage of data for decision-making, and extensive collaboration with local and global partners and funders. Lessons learned include the significant time it takes to scale and institutionalize a digital health systems innovation due to the time to generate evidence, change opinions, and build capacity.Conclusions:Jamii ni Afya represents one of the world’s first examples of a nationally scaled digitally enabled community health program. This implementation report outlines key successes and lessons learned, which may have applicability to other governments and partners working to sustainably strengthen primary health systems.