Nevin Manimala

Stud Health Technol Inform. 2026 May 7;335:163-168. doi: 10.3233/SHTI260077.

ABSTRACT

Standardized care plans are essential for improving care quality, enabling consistent care delivery and seamless data exchange across care settings. However, the extent to which existing care planning tools implement standards, like HL7 FHIR, remains unclear. This study evaluates existing care planning tools through a fit-gap analysis assessing their alignment with standardized, interoperable care plan requirements and identify gaps hindering standardization and system integration. Tools were evaluated based on standard compliance, particularly FHIR, output capabilities, geographical origin, and accessibility patterns. Nine tools demonstrated full FHIR compliance, two partial implementation, and five no explicit FHIR support. Geographic analysis revealed US and Australian tools exhibited higher FHIR implementation rates than European solutions. Only seven tools achieved the “fit” criterion of combining care planning functionality with full FHIR compliance. No tools from Asia, Africa, or South America were identified, suggesting global gaps in access to standardized care planning technologies. These findings highlight geographic disparities in interoperability standards adoption and a need for policy interventions, particularly in Europe and underrepresented regions.

PMID:42119113 | DOI:10.3233/SHTI260077

Stud Health Technol Inform. 2026 May 7;335:153-154. doi: 10.3233/SHTI260074.

ABSTRACT

Recruiting patients for medical research requires a balance between ethical transparency and practical feasibility. We examined a two-stage re-consenting process for patients in the context of biobanks, capturing trends in patient engagement and preferences regarding future data use. Participation declined mainly at the opt-in stage, highlighting early procedural barriers, while among consenting, most participants allowed broad secondary use of their data without additional recontacting. In general, the results obtained support a transparent opt-out solution for data donation and secondary use of health data.

PMID:42119110 | DOI:10.3233/SHTI260074

Stud Health Technol Inform. 2026 May 7;335:131-136. doi: 10.3233/SHTI260069.

ABSTRACT

BACKGROUND: Serious games (SGs) with hand-tracking solutions are increasingly adopted in neurological rehabilitation. The Leap Motion Controller (LMC) is widely used in clinical and research contexts, while software-based systems, such as MediaPipe (MP), are emerging.

OBJECTIVES: This study compared the usability of MP- and LMC-based SGs.

METHODS: Seven SGs were developed to evaluate Grasp and Pinch gestures and tested with fifty healthy volunteers. Usability was assessed with the System Usability Scale (SUS) after each device, and an ad-hoc comparative questionnaire (LMC, MP, Neutral) collected preferences on several aspects.

RESULTS: Both systems achieved excellent SUS scores (MP: 88/100; LMC: 90/100), with no statistical difference in usability. The comparative questionnaire showed a stronger preference for LMC (63%) versus MP (17%), with 20% neutral.

CONCLUSION: Although both solutions demonstrated high usability, LMC was more frequently preferred in direct comparison. The SUS results of MP encourage further investigation, including large-scale tests with neurological patients.

PMID:42119105 | DOI:10.3233/SHTI260069

Stud Health Technol Inform. 2026 May 7;335:96-101. doi: 10.3233/SHTI260062.

ABSTRACT

BACKGROUND: Primary health care forms the backbone of comprehensive and patient-centered healthcare in Austria and Germany. Particularly in rural areas, there are increasing gaps in local patient care.

OBJECTIVES: Hybrid health care services should ensure multidisciplinary primary health care at the community level.

METHODS: A mixed-methods approach consisting of a semi-structured literature review and an online survey of mayors and heads of administration in municipalities in Upper Austria and Upper Bavaria (12/2025, N=757, n=78, rr=10.3%) was conducted.

RESULTS: An average level of digital maturity was identified in municipalities of Upper Austria and Upper Bavaria. Furthermore, the future establishment and expansion of digital health care services was identified as a key lever.

CONCLUSION: In the future, primary health care at the community level will be characterized by hybrid healthcare services.

PMID:42119098 | DOI:10.3233/SHTI260062

Stud Health Technol Inform. 2026 May 7;335:69-77. doi: 10.3233/SHTI260057.

ABSTRACT

BACKGROUND: Digitalization in healthcare promises efficiency gains, in the present case particularly addressing medication management. In mobile care, staff often spend substantial time traveling to collect patients’ health insurance cards, visit physicians to obtain prescriptions, pick up medication at pharmacies, and deliver it to patients. The Linked Care project developed an integrated IT solution to streamline medication reordering in mobile care settings.

OBJECTIVES: To evaluate whether an electronic system improves the efficiency and quality of the medication reordering process compared to usual care.

METHODS: A non-randomized controlled trial allocated nurses and caregivers to intervention (Linked Care) or control groups. Standardized and project-specific self-reported outcomes on workload (psychological stress), time expenditure, errors & information loss, and usability were obtained via online questionnaires. Data was analyzed using mixed-effects models as well as repeated measures ANOVA for trend analyses.

RESULTS: Workload was consistently lower in the intervention group, whereas time expenditure showed no significant differences. Errors and information loss improved significantly in the 6-month follow-up. Usability ratings ranged from “excellent” to “good”.

CONCLUSION: Using Linked Care was associated with reduced self-perceived workload (psychological stress) and improved error prevention, with high usability; however, self-perceived time savings were negligible.

PMID:42119093 | DOI:10.3233/SHTI260057

Stud Health Technol Inform. 2026 May 7;335:53-54. doi: 10.3233/SHTI260054.

ABSTRACT

While health technology assessment (HTA) aims to quantify the quality of digital health apps, app stores offer unstructured information on app quality to users like patients. This provides barriers for people with Parkinson’s disease (PD) and Alzheimer’s disease (AD) to identify high-quality apps. This study explores the discoverability of AD/PD-related apps and their quality information in app stores. We applied descriptive statistics and text mining such as large language models (LLMs) and topic modelling to analyze app descriptions and user reviews of 1237 apps. Only ∼2% of apps were discoverable as holding a CE-mark. Most apps were “Care Support”, followed by “Health & Wellness” and “Patient Monitoring” patient-facing categories. User reviews addressed “user experience”, “health improvement”, and “costs”. To help patients identify high-quality apps, quality information should be presented in a structured and trustworthy way. The use of user feedback for patient-reported measures should be explored in future work.

PMID:42119090 | DOI:10.3233/SHTI260054

Stud Health Technol Inform. 2026 May 7;335:14-21. doi: 10.3233/SHTI260048.

ABSTRACT

Misinformation increasingly undermines trust in childhood immunization among both the public and healthcare workers. Innovative, scalable educational approaches are needed to strengthen vaccine confidence across diverse settings. Objectives: To describe and evaluate a global initiative delivering asynchronous online vaccination courses, focusing on participant reach, completion, and qualitative experiences. We conducted a pilot evaluation using descriptive analysis of enrollment and completion data, combined with qualitative content analysis of voluntarily submitted open-text feedback. Courses were fully online, self-paced, accredited for continuing professional development, and implemented through international, local, and institutional partnerships. Between April 2024 and December 2025, seven courses were delivered. A total of 3,018 participants from 75 countries completed at least one course, with an overall completion rate of 31.5%. Sixty feedback entries from low-, middle-, and high-income settings were analyzed. Nine themes emerged, with differing emphases across contexts. Asynchronous online vaccination education can decentralize knowledge at scale, support trust-building and empowerment, and complement traditional immunization communication strategies.

PMID:42119084 | DOI:10.3233/SHTI260048

Anal Chem. 2026 May 12. doi: 10.1021/acs.analchem.6c00454. Online ahead of print.

ABSTRACT

Cerebrospinal fluid (CSF) orexin-A is the gold-standard biomarker for narcolepsy type 1 (NT1), however, conventional radioimmunoassays (RIA) often suffer from cross-reactivity and overestimation, fueling long-standing conundrum regarding orexin detection in peripheral blood. In this study, we developed an ultrasensitive LC-MS/MS method (LLOQ = 0.1 pg/mL) incorporating a streamlined one-step protein precipitation protocol coupled with acid-shielding and cocktail-protection strategies to mitigate severe nonspecific adsorption and enzymatic degradation. Verification in paired CSF and blood samples from narcolepsy patients and controls revealed that orexin-B and peripheral orexins remain consistently below the detection limit (<0.1 pg/mL), proving that previously reported ng/mL levels in blood are analytical artifacts. Our LC-MS/MS approach resolved a 50-fold quantitative overestimation by RIA and significantly improved diagnostic resolution for narcolepsy type 2 (AUC = 0.73, P < 0.05) where RIA failed to achieve statistical significance (P = 0.297). This study establishes a high-specificity analytical framework as a practical reference standard for refined sleep disorder diagnostics and confirms that CSF orexin-A remains the currently the most reliable clinical biomarker.

PMID:42118575 | DOI:10.1021/acs.analchem.6c00454

Cancer. 2026 May 15;132(10):e70430. doi: 10.1002/cncr.70430.

ABSTRACT

BACKGROUND: Diabetes is associated with an increased risk of cancer; however, few epidemiological studies of diabetes and cancer risk have focused on Hispanic/Latino adults. This study examined the associations between three time-varying measures of diabetes and cancer incidence among participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

METHODS: HCHS/SOL is a multi-site prospective cohort study of 16,415 Hispanic/Latino adults. Time-varying diabetes measures (assessed at visit 1: 2008-2011 and visit 2: 2014-2017) included diabetes status (no diabetes, pre-diabetes, and diabetes), glycemic control (no diabetes, pre-diabetes, controlled diabetes [hemoglobin A_1c (HbA_1c) <7.0%], and uncontrolled diabetes [HbA_1c ≥7.0%]), and insulin resistance (no diabetes, pre-diabetes + Homeostatic Model Assessment of Insulin Resistance [HOMA-IR] <3.0, pre-diabetes + HOMA-IR ≥3.0, diabetes + HOMA-IR <3.0, and diabetes + HOMA-IR ≥3.0). Incident cancers diagnosed from visit 1 through 2021 were identified through state cancer registry linkages; 715 cancers including 330 obesity-related cancers (ORCs) were diagnosed over a mean follow-up of 10.7 years. The authors used survey-weighted marginal structural Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for each time-varying diabetes measure and overall cancer and ORC risk, adjusting for demographic, social, and behavioral characteristics.

RESULTS: Time-varying pre-diabetes and diabetes (vs. no diabetes) status were associated with cancer HRs of 1.82 (95% CI, 1.27-2.61) and 2.49 (95% CI, 1.65-3.74), respectively. HRs were further elevated among those with diabetes + HbA_1c ≥7.0% (HR, 3.12; 95% CI, 1.44-6.79) and those with diabetes + HOMA-IR ≥3.0 (HR, 2.78; 95% CI, 1.69-4.56). Associations were stronger for ORC risk; however, estimates were less precise.

CONCLUSION: Diabetes is associated with increased risk of cancer and ORC. Diabetes prevention and control may be additionally important for cancer prevention among Hispanic/Latino adults.

PMID:42118574 | DOI:10.1002/cncr.70430

JMIR AI. 2026 May 12;5:e84315. doi: 10.2196/84315.

ABSTRACT

BACKGROUND: Regulation of artificial intelligence (AI) has been slow relative to the pace of its integration into health care. Several AI diagnostic tools for diabetic retinopathy (DR) have already received Food and Drug Administration (FDA) clearance, making it a timely and concrete example for exploring public perspectives on regulatory approval. The scope of FDA regulation of AI tools is being explored, and public attitudes about regulatory oversight should inform these discussions and are explored in this paper. Prior research suggests that comfort, trust, and political orientation shape views on government regulation and emerging technologies, potentially affecting support for oversight of AI in health care.

OBJECTIVE: This study assessed the perceived importance of FDA approval for AI-supported clinical decision support tools, with DR as the use case. We explored how comfort with AI tool developers, trust in data sharing, political affiliation, and demographic characteristics relate to the importance of FDA approval among US adults.

METHODS: A national survey was conducted in 2023 using the NORC AmeriSpeak Panel, a probability-based sample including 1787 respondents, with a subset of 982 participants answering questions about a use case describing an AI tool for identifying DR. Participants rated the importance of FDA approval for such tools on a 4-point Likert scale, with responses dichotomized between high and low perceived importance. Logistic regression models assessed associations between this outcome and predictors including comfort with AI tool developers, trust in data sharing, political affiliation, and demographic characteristics.

RESULTS: Among the 982 respondents presented with the DR use case, 658 (67%) indicated that FDA approval was “fairly” or “very” important. Statistically significant factors associated with the outcome (“It is important that the AI tool is approved by the FDA”) included higher comfort with using the tool (odds ratio [OR] 1.44, 95% CI 1.11-1.87; P=.006), comfort with developers from private companies (OR 1.38, 95% CI 1.09-1.76; P=.008), and hospitals (OR 1.60, 95% CI 1.25-2.05; P<.001). Trust in responsible data sharing (OR 1.25, 95% CI 1.05-1.5; P=.01) and higher education (OR 1.64, 95% CI 1.02-2.62; P=.04) also predicted higher support. Lean or strong Republicans (OR 0.43, 95%CI 0.3-0.6; P<.001) and Independents (OR 0.63, 95% CI 0.42-0.96; P=.03) were less likely to view FDA approval as important, as were Black (OR 0.50, 95% CI 0.34-0.77; P<.001) and Hispanic (OR 0.57, 95% CI 0.38-0.86; P=.007) respondents compared with White respondents.

CONCLUSIONS: This study offers insights into public attitudes regarding FDA oversight of AI-based clinical decision support tools. Findings highlight how comfort, trust, and lower confidence from marginalized communities and some political groups shape perceived importance of FDA approval, offering a point for broader applications in health care AI governance. These factors should be better considered as health systems work to ensure trustworthy implementation of new AI technologies.

PMID:42118568 | DOI:10.2196/84315