Healthcare Platform Research Articles

Applying the PRECEDE-PROCEED model to develop MommaConnect: a digital healthcare platform for addressing postpartum depression and improving infant well-being.

The PRECEDE-PROCEED model is a comprehensive planning and theoretical framework that incorporates epidemiological, environmental, behavioral, and social factors systematically to design, implement, and evaluate health promotion programs. As such, PRECEDE-PROCEED is a highly effective tool for addressing complex and significant public health concerns like postpartum depression (PPD). PPD negatively impacts mothers and their infants, with studies showing that approximately one in eight mothers experience PPD, leading to adverse effects on maternal functioning and infant development. However, access to specialized evidence-based treatment remains significantly limited due to barriers including social determinants of health. This paper explores the application of the PRECEDE-PROCEED model as a planning and theoretical framework for the design and development of MommaConnect, an innovative digital healthcare platform aimed at reducing PPD symptoms and improving maternal-infant interaction while overcoming barriers to treatment. Key components of the MommaConnect design and development process are mapped onto the steps of the PRECEDE-PROCEED model. MommaConnect features are aligned with specific stages of the model, from assessing, predisposing, enabling, and reinforcing factors to designing, implementing, and evaluating the intervention. By leveraging this model, MommaConnect represents a promising innovative approach to address PPD to improve maternal functioning and infant health in a digitally-enabled era. This paper underscores the importance of utilizing a framework like the PRECEDE-PROCEED model in the design and development of innovative healthcare solutions.

A novel ALM based security framework for block chain in healthcare platform

A novel ALM based security framework for block chain in healthcare platform

Synergistic Strategies of Biomolecular Transport Technologies in Transdermal Healthcare Systems.

Transdermal healthcare systems have gained significant attention for their painless and convenient drug administration, as well as their ability to detect biomarkers promptly. However, the skin barrier limits the candidates of biomolecules that can be transported, and reliance on simple diffusion poses a bottleneck for personalized diagnosis and treatment. Consequently, recent advancements in transdermal transport technologies have evolved toward active methods based on external energy sources. Multiple combinations of these technologies have also shown promise for increasing therapeutic effectiveness and diagnostic accuracy as delivery efficiency is maximized. Furthermore, wearable healthcare platforms are being developed in diverse aspects for patient convenience, safety, and on-demand treatment. Herein, a comprehensive overview of active transdermal delivery technologies is provided, highlighting the combination-based diagnostics, therapeutics, and theragnostics, along with the latest trends in platform advancements. This offers insights into the potential applications of next-generation wearable transdermal medical devices for personalized autonomous healthcare.

Physician Adoption of AI Assistant

Problem definition: Artificial intelligence (AI) assistants—software agents that can perform tasks or services for individuals—are among the most promising AI applications. However, little is known about the adoption of AI assistants by service providers (i.e., physicians) in a real-world healthcare setting. In this paper, we investigate the impact of the AI smartness (i.e., whether the AI assistant is powered by machine learning intelligence) and the impact of AI transparency (i.e., whether physicians are informed of the AI assistant). Methodology/results: We collaborate with a leading healthcare platform to run a field experiment in which we compare physicians’ adoption behavior, that is, adoption rate and adoption timing, of smart and automated AI assistants under transparent and non-transparent conditions. We find that the smartness can increase the adoption rate and shorten the adoption timing, whereas the transparency can only shorten the adoption timing. Moreover, the impact of AI transparency on the adoption rate is contingent on the smartness level of the AI assistant: the transparency increases the adoption rate only when the AI assistant is not equipped with smart algorithms and fails to do so when the AI assistant is smart. Managerial implications: Our study can guide platforms in designing their AI strategies. Platforms should improve the smartness of AI assistants. If such an improvement is too costly, the platform should transparentize the AI assistant, especially when it is not smart. Funding: This research was supported by a Behavioral Research Assistance Grant from the C. T. Bauer College of Business, University of Houston. H. Zhao acknowledges support from Hong Kong General Research Fund [9043593]. Y. (R.) Tan acknowledges generous support from CEIBS Research [Grant AG24QCS]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0093 .

Qualitative Study to Identify Strengths, Weakness, Opportunities, and Challenges of Family Adoption Programs among Students.

National Medical Commission (NMC) in their recent notification included Family Adoption Program (FAP) in the undergraduate curriculum to provide a learning opportunity towards community-based health care to Indian medical graduates. This study is carried out to explore and know strengths, weaknesses, opportunities, and challenges of FAP. FAP were used to gather data using Focus Group Discussion (FGD) and in-depth interviews of the stakeholders of the program. FGD of students was conducted. In-depth interviews of families, Sarpanch, Panchayat members, ASHA workers of the village, faculties, and Head of the Department of Community Medicine were conducted. Data analysis was done by using deductive-inductive content analysis method using computer software NVivo. Four main categories or themes were formed: strengths, weaknesses, opportunities, and challenges. Strengths include increased understanding about Community Medicine subject, beneficence to students, and community. Weakness includes difficulties in field with respect to time and availability and implementation of programs. Opportunities include early field exposure and FAP as a platform for primary healthcare. Challenges include competencies not aligned with phasewise curriculum and difficulties in adopting five families per student. The family adoption program needs to be adopted and implemented as a part of curriculum for MBBS students as there are many strengths and opportunities, while weaknesses and challenges need to be addressed.

The faster or richer the response, the better performance? An empirical analysis of online healthcare platforms from a competitive perspective

The emergence of online healthcare platforms has changed the competitive environment among physicians. However, little is known about how physicians can improve their performance in this new environment. Platforms also face challenges in comprehending the competitive mechanisms among physicians, which might hinder them from formulating strategic managerial decisions that foster sustained growth. In this light, we extract medical service-related information from physicians' response behavioral data on a prominent healthcare platform, and empirically investigate the factors affecting physicians' online performance from a competitive perspective as well as the gender differences in these effects. The results indicate that physicians' responses significantly impact their online performance, revealing a competitive relationship between physicians and their colleagues in the same department. Specifically, a fast response time and informative responses are positively correlated with the focal physician's performance, whereas colleagues' informative responses negatively impact the focal physician's performance, and this relationship is mediated by the focal physician's response informativeness. Nevertheless, there is no significant correlation between colleagues' response time and the focal physician's performance. The results also unveil that gender moderates the effect of response informativeness on the focal physician's performance. Specifically, colleagues' response informativeness has a more significant impact on male physicians' performance than on female physicians' performance, suggesting a greater propensity for competition among male physicians. Our findings could offer decision support for enhancing physician performance and healthcare platform management.

Impact of an End-User-Designed Education Bundle for Electronic Health Records System Education of House Officers.

Introduction The benefits of Electronic Health Records (EHR)use in clinical care are well documented. However, without proper education and training on EHR systems, clinicians may face challenges in utilizing these technological tools effectively. Suboptimal usage of EHR systems can affect productivity.This study assesses the effectiveness of an end-user-designed education bundle as a supplement to existing training in EHR training for house officers. Additionally, it evaluates the effectiveness of using non-conventional teaching modalities (i.e., short TikTok-style videos)to see how effective and accepted it was in comparison to traditional educational material. Methods A single-armed pre-post-study design consisting of 36 house officers was employed to evaluate the effectiveness of the intervention bundle. The bundle consists of a series of EHR tips and tricks as identified by experienced senior medical officers. The three components of the bundle are a handbook with consolidated tips and tricks, a long-form lecture video, and a series of TikTok-style videos. Distribution was done through healthcare collaborative platforms such as TigerConnect™ (Los Angeles,USA) and email. Results Participants found that the inclusion of our supplementary education bundle results in more effective training for EHR usage, with mean effectiveness with and without the educational bundle being 7.77 and 6.44, respectively (p < 0.001). There were also significant improvements in ease of finding information (7.67 vs 7.14, p = 0.016), performing general functions (7.50 vs 6.89, p = 0.0050), and overall efficiency (7.39 vs 6.92, p = 0.022). We also found TikTok-style videos were non-inferior to more traditional forms of education such as a handbook and traditional long-form lecture videos (p = 0.250). Conclusion An end-user-driven education bundle focusing on high-yield, advanced functions may be useful in enhancing the overall EHR system experience for junior doctors. Of note, TikTok-style videos may be no less effective than traditional methods of EHR teaching.

Mobility-enhanced delay-aware cloudlet movement and placement using cluster-based technique in a smart healthcare platform

Mobility-enhanced delay-aware cloudlet movement and placement using cluster-based technique in a smart healthcare platform

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine.

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Perspectives on competency-based feedback for training non-specialists to deliver psychological interventions: multi-site qualitative study of the EQUIP competency-based approach.

The use of feedback to address gaps and reinforce skills is a key component of successful competency-based mental health and psychosocial support intervention training approaches. Competency-based feedback during training and supervision for personnel delivering psychological interventions is vital for safe and effective care. For non-specialists trained in low-resource settings, there is a lack of standardised feedback systems. This study explores perspectives on competency-based feedback, using structured role-plays that are featured on the Ensuring Quality in Psychosocial and Mental Health Care (EQUIP) platform developed by the World Health Organization and United Nations Children's Fund. Qualitative data were collected from supervisors, trainers and trainees from multiple EQUIP training sites (Ethiopia, Kenya, Lebanon, Peru and Uganda), from 18 key informant interviews and five focus group discussions (N = 41 participants). Qualitative analysis was conducted in Dedoose, using a codebook with deductively and inductively developed themes. Four main themes demonstrated how a competency-based structure enhanced the feedback process: (a) competency-based feedback was personalised and goal-specific, (b) competency-based feedback supported a feedback loop, (c) competency-based feedback supported a comfortable and objective feedback environment, and (d) competency-based feedback created greater opportunities for flexibility in training and supervision. A better understanding of the role of feedback supports the implementation of competency-based training that is systematic and effective for trainers and supervisors, which ultimately benefits the learning process for trainees.

13‐1: Invited Paper: New Frontier in Display Technology: OPD Sensor in OLEDs for Healthcare Application

People's interest in health care has increased these days. As various health care platforms are emerging, the corresponding market is also expanding. In order to monitor health conditions in everyday life, no physical damage should be made. One of the non‐invasive method, using light for sensing, is preferred due to this effect. In general, as the wavelength increases, light can penetrate deeper into the skin, leading to different bio‐signals. For example, All‐in‐One Sensor Display, which was announced by Samsung Display at SID 2023, used green light to selectively sense fingerprints on the skin surface by reducing the depth of skin penetration. To apply this progress to health care, red light can also be used for OPDs in the All‐in‐One Sensor Display. In this case, red light from red pixel is used as the light source. The light can reach blood vessels under the epidermis because the skin penetration depth is deeper than green light. The light reaching the blood vessel becomes a signal to gather biometric information. This biometric information is then collected at OPD as the signal reaches the OPD in the panel again. For these series of processes, an OPD capable of effective detecting of light in the 625 nm region emitted by red pixels is required. In this study, OPD was fabricated using RD‐01, a high absorption donor material suitable for red light absorption, and RA‐01, a non‐fullerene acceptor for mass production, to respond to red light near 625 nm. The EQE of the OPD was 25% at wavelength of 625 nm. In addition, absorption spectrum of the OPD covers the Red emission effectively. Our results represent that OLED panel can be used for not only fingerprint recognition but also health care platform.

Basic Directions of Designing a Health-care platform by Life Cycle for Working Women

Basic Directions of Designing a Health-care platform by Life Cycle for Working Women

Developing a single‐session outcome measure using natural language processing on digital mental health transcripts

AbstractBackgroundCurrent outcome measures in digital mental health lack granularity, especially for single‐session interventions. This study aimed to address this by utilising natural language processing (NLP) methods to create a clear and relevant outcome measure. This paper describes the development of the Adult Session Wants and Needs Outcome Measure (Adult SWAN‐OM), a novel outcome measure for the Qwell digital mental healthcare platform to understand service user (SU) needs engaging in single‐session therapy (SST).MethodsThe research employs a multi‐phased approach combining NLP methods with the typical stages of outcome measures development as follows: (1) assumption definition and validation with SUs and clinicians; (2) transcript theme extraction using the RoBERTa large language model (LLM) in conjunction with topic modelling to extract themes from 254 single‐session transcripts from 192 SUs; (3) clinical item refinement focus group; (4) content validity with clinicians and SUs to improve the relevance and clarity of the items; and (5) outcome measure finalisation in a workshop held with clinicians to consolidate the final wording.ResultsNinety‐six potential wants and needs were generated and distilled into 12 measure items. The outcome measure was shown to be relevant and clear to both SUs and clinicians when used in the context of SST.ConclusionThis study highlights the potential of combining NLP approaches with co‐creation methods in single‐session outcome measure development. We argue that the incorporation of clinical expertise and SU experience ensures the clarity and applicability of such measures and that this approach to capturing single‐session wants and needs promises novel insights for supporting digital mental health interventions.

HealthSquare: A Survey of Deep Learning Based Healthcare Platform

The multi-health diagnosis web application HealthSquare represents a pioneering endeavor in healthcare technology by integrating advanced AI models for comprehensive health analysis. The system comprises multiple components, including two image processing models for fracture detection from X-ray reports and brain tumor identification from MRI scans. Additionally, GCP’s Vertex AI is leveraged for blood &amp; thyroid report analysis to predict &amp; provide descriptive diagnosis. Moreover, the platform enables users to create accounts for secure storage of medical reports, akin to a specialized digital locker tailored specifically for healthcare documents. Furthermore, an AI-driven chat bot offers real-time health- related guidance and recommendations to users based on their queries. The integration of these components aims to redefine healthcare diagnostics by providing users with accessible, accurate, and personalized health analysis tools. Utilizing advanced image processing techniques, AI-driven analyses, and secure user accounts, the project strives to revolutionize healthcare by offering a comprehensive and user-centric approach to medical diagnosis and health management. Keywords: AI Integration, Fracture Detection, Brain Tumor Identification, Thyroid Detection, Blood Analysis, Secure Medical Report Storage, AI Chat Bot.

The Digital Medic: Assessing the Role of Social Media in Empowering Individuals with Health Knowledge and Choices

Purpose: The rapid expansion of social media platforms has significantly transformed communication patterns and information access. This study examines the role of social media in sharing health-related content among Indian users, evaluating its trust, influence, and impact. Methodology: A questionnaire survey was conducted among 100 people using SPSS v24, G-power, and literature reviews. Statistical methods assessed reliability, trust, influencing behaviour, and impact perspectives. A model was developed using SPSS 21 AMOS software. Findings: This study examines the transformative potential of social media platforms like YouTube and Instagram for health-oriented information dissemination. It found a significant association between gender and the use of social media for medical information, with independent variables including reliability and Trust, Influence and Behaviour Impact and perspective, and positive impact. The value of ANOVA in the regression model was 0.00, indicating a better fit for the regression model. Practical Implication: The effectiveness of tools in decision-making is crucial for organisations like fitness clubs, gyms, and yoga centres to boost publicity, increase consumer base, and achieve higher revenues, while general people can find trustworthy healthcare information platforms.

Status of MyHealthWay and Suggestions for Widespread Implementation, Emphasizing the Utilization and Practical Use of Personal Medical Data.

In the Fourth Industrial Revolution, there is a focus on managing diverse medical data to improve healthcare and prevent disease. The challenges include tracking detailed medical records across multiple institutions and the necessity of linking domestic public medical entities for efficient data sharing. This study explores MyHealthWay, a Korean healthcare platform designed to facilitate the integration and transfer of medical data from various sources, examining its development, importance, and legal implications. To evaluate the management status and utilization of MyHealthWay, we analyzed data types, security, legal issues, domestic versus international issues, and infrastructure. Additionally, we discussed challenges such as resource and infrastructure constraints, regulatory hurdles, and future considerations for data management. The secure sharing of medical information via MyHealthWay can reduce the distance between patients and healthcare facilities, fostering personalized care and self-management of health. However, this approach faces legal challenges, particularly relating to data standardization and access to personal health information. Legal challenges in data standardization and access, particularly for secondary uses such as research, necessitate improved regulations. There is a crucial need for detailed governmental guidelines and clear data ownership standards at institutional levels. This report highlights the role of Korea's MyHealthWay, which was launched in 2023, in transforming healthcare through systematic data integration. Challenges include data privacy and legal complexities, and there is a need for data standardization and individual empowerment in health data management within a systematic medical big data framework.

A practical model for effective eye care delivery in Southeast Asian rural communities: A proposal built based on experts' recommendations.

To evaluate rural community-based eye care models from the perspective of community ophthalmology experts and suggest sustainable technological solutions for enhancing rural eye care delivery. A semi-structured descriptive survey, using close-ended and open-ended questions, was administered to the experts in community ophthalmology sourced through purposive sampling. The survey was self-administered and was facilitated through online platforms or in-person meetings. Uniform questions were presented to all participants, irrespective of their roles. Surveyed participants (n = 22 with 15 from India and 7 from Nepal) in high-volume tertiary eye hospitals faced challenges with resources and rural outreach. Participants had mixed satisfaction with pre-operative screening and theatre resources. Delayed presentations and inexperienced surgeons contributed to the surgery complications. Barriers to rural eye care included resource scarcity, funding disparities, and limited infrastructure. In rural/peri-urban areas 87% of participants agreed with providing primary eye care services, with more than 60% of the experts not in agreement with the makeshift center model of eye care delivery. Key components for an effective eye care model are sustainability, accessibility, affordability, and quality. These can be bolstered through a healthcare management platform and a human-chain supply distribution system. Tailored interventions are crucial for rural eye care, emphasizing the need for stronger human resources, optimized funding, and community awareness. Addressing challenges pertinent to delayed presentation and surgical training is vital to minimizing complications, especially with advanced cataracts. Enhancements in rural eye care demand a comprehensive approach prioritizing accessibility, affordability, and consistent quality.

IJCM_134A: Community Engagement Strategies for Prevention and Control of Non-communicable Diseases in Primary Healthcare Settings: A review of evidences from Low- and Middle-income countries

Background: Community engagement is considered as one of the pillars in successful implementation program for prevention and control of Non- Communicable Diseases (NCDs). Objective: To gather evidence from the available literature concerning effective strategies for enhancing community engagement in primary healthcare for NCD prevention, which are appropriate for resource constraint settings in low- and middle-income countries. Methodology: This study followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis for Scoping Reviews (PRISMA- ScR) guidelines. Original articles and reports published from January 2000 to September 2023, in English language; with the following designs- Randomized controlled trials with single or multiple arms, quasi- experimental studies, public health practices and qualitative studies, available online were included in this review. Digital databases like PubMed and Google scholar were searched to find out articles, using a pre-designed peer- reviewed search strategy by combining established search terms and free text terms. Further, hand search was made by going through the references of the retrieved articles. All publications were independently evaluated by two reviewers. Information extracted from the published articles were recorded using a pre-designed data extraction sheet in Microsoft excel. Results: Key interventions emerged from the review included group-based peer support program; family level intervention; setting based approach (Health Promoting school /institution /market /hospital); advocacy, network and counselling among People Living with NCDs etc. Evidence also suggested use of appropriate technology such as CPHC-NCD Software, generation of Ayushman Bharat-Health Account IDs (ABHA IDs) through National NCD Portal, maintenance of NCD data record and register in digital formats on National NCD Portal and spreading community dialog using mass media, digital media and social media. Conclusion: The findings of this study will guide the development and implementation of evidence-based interventions for prevention of NCDs using primary healthcare platform suitable for resource limited settings.

Integrating IoT with Virtual Healthcare: A theoretical framework for enhancing accessibility and efficiency in the U.S. healthcare sector

This paper presents an open source platform aimed at revolutionizing healthcare delivery in the United States by significantly reducing the cost of virtual healthcare software. The proposed platform addresses the pressing challenges of accessibility and efficiency in the U.S. healthcare sector through cloud native backend solutions and mobile apps driven by modular design and IoT-enabled devices. By leveraging IoT technology, virtual healthcare platforms can provide real-time monitoring, remote consultations, and personalized care, thereby improving patient outcomes and reducing healthcare costs. The platform emphasizes nationwide scalability, inclusivity, and a transition towards a proactive, patient-centric healthcare model. By leveraging the opensource community, modular design and a plugin architecture, it aims to enable feature extensibility beyond what is foreseeable today, promote interoperability, ensure data privacy and security, and promote technology adoption among healthcare providers and patients. The proposal presented in this paper lays the foundation for the implementation aimed at harnessing the full potential of IoT and virtual healthcare to enhance accessibility and efficiency in the U.S. healthcare sector, ultimately advancing the quality of care and well-being of the population.

An Effective Doctor Recommendation Algorithm for Online Healthcare Platforms

The emergence of online healthcare platforms provides patients with convenience, but choosing the right doctor among the thousands of doctors available on these platforms has become a challenge for patients. The majority of these platforms recommend the same doctors to all patients based on a global ranking, disregarding individual patient preferences. The use of recommender systems helps to resolve this issue by assisting patients in locating doctors who meet their preferences and requirements. Particularly, Collaborative Filtering (CF) algorithms have been extensively utilized to generate personalized recommendations for a variety of applications. Despite their success, they still need to be further optimized to address both the sparsity and cold-start problems due to insufficient data. In this paper, we propose an effective doctor recommendation approach to assist patients in searching for satisfactory doctors who precisely match their preferences regardless of time and location. The proposed approach employs Multi-Criteria CF and content filtering to enhance the quality of recommendations by mitigating the impact of data sparsity and cold start challenges. Offline tests conducted on a real-world dataset show that the proposed approach is superior to state-of-the-art approaches in addressing the aforementioned issues and boosting prediction accuracy and coverage.