A hybrid AHP–TOPSIS decision-support framework for evaluating mental health support alternatives in higher education

A user centered evaluation framework for mobile health applications using Fuzzy AHP and TOPSIS: evidence from expert reviews and user experience data

Background The rapid growth of digital mental health applications has created a need for comprehensive evaluation approaches that simultaneously consider clinical effectiveness, user experience, and ethical requirements. Existing assessment methods often focus on limited aspects of application quality and may not adequately capture uncertainty in expert judgments. Objective This study aimed to develop and apply a hybrid Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework for the user-centered evaluation and ranking of digital mental health applications. Methods A publicly available Mobile Health Apps Evaluation Dataset comprising 62 applications and 274 evaluation records was analyzed. Four main criteria and twelve subcriteria were established through expert consultation, including usability, engagement, clinical relevance, and ethics and privacy. An expert panel consisting of 18 specialists from public health, healthcare, digital health, health informatics, and user experience fields participated in the weighting process. Fuzzy AHP was employed to determine criteria weights under uncertainty, while TOPSIS was used to rank the selected applications. Sensitivity analysis was conducted to evaluate the robustness of the ranking outcomes. Results Clinical relevance received the highest importance weight (0.352), followed by ethics and privacy (0.284), usability (0.211), and engagement (0.153). Among the subcriteria, clinical expert involvement (0.132), privacy policy availability (0.118), and guideline compliance (0.115) were identified as the most influential factors. The TOPSIS analysis ranked A1, A2, and A3 as the highest-performing applications, with closeness coefficients of 0.842, 0.816, and 0.791, respectively. Sensitivity analysis demonstrated strong ranking stability across alternative weighting scenarios, with Spearman correlation coefficients exceeding 0.90. Conclusion The proposed hybrid Fuzzy AHP–TOPSIS framework provides a robust and transparent approach for evaluating digital mental health applications. The findings highlight the importance of clinical credibility, privacy protection, and evidence-based design in determining application quality. The framework offers practical guidance for developers, healthcare professionals, and policymakers seeking to identify and promote high-quality digital mental health technologies.

['Shared Steps & Checks': A pragmatic model for shared decision making in mental health care].

Toward hybrid mental health care: an early health technology assessment based on stakeholder analysis and economic simulations

Abstract Objectives Combining digital and traditional treatment elements as part of a hybrid care delivery model may strengthen the sustainability of mental health care. However, implementation problems permeate efforts of digitalizing routine care. The objective of this formative study is to explore current unmet stakeholder needs and the potential value of hybrid care models for the treatment of severe mental disorders, in order to guide further research and inform practical implementation strategies. Methods We applied Step Up, an early health technology assessment (eHTA) framework. We performed (a) a template analysis of qualitative data from a stakeholder workshop mapping unmet needs, potential effects of hybrid care models, and conditions relevant for implementation and (b) exploratory economic simulations of the impact of increasing video consultation use in routine services on quantitative sustainability parameters. Results The template analysis revealed stakeholder-anticipated value from hybrid care models across all domains: patient, clinician, and next-of-kin user experiences; clinical variables; organizational setup; and economy. Two recommendations for enhancing implementation were identified: promoting nuanced perceptions of digitalized care and developing clinician incentives. The simulations demonstrated that a moderate increase in video consultations may impact clinical capacity, as well as economic and environmental sustainability. Conclusions This study provides early decision support concerning hybrid care models during the conceptual stage of service innovation. It reveals that conservative changes toward digitalized practices can create new opportunities for organizing services and have a substantial impact on sustainability parameters. Successful implementation across clinical contexts requires continuous consideration of the clinician user perspective, starting from the conceptual phase.

A systematic review of the impact of Mental Health First Aid on medical, nursing and allied healthcare professional students

Background Healthcare professional (HCP) students are at high risk of mental health problems, but stigma and fear of career repercussions often deter them from seeking help. Mental Health First Aid (MHFA) is a globally disseminated course teaching the public to identify and respond to people experiencing mental health problems. MHFA training may address some of the challenges faced by HCP students, by improving mental health knowledge and by enhancing well-being and peer support. Aims To systematically review the available literature regarding the impact of MHFA training on HCP students’ mental health literacy, confidence and intentions to provide help, stigma, peer support and self-care. Method Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (International Prospective Register of Systematic Reviews ID: CRD42024589509), five databases were searched. Primary studies evaluating the above outcome measures in HCP students were included. Two authors independently screened references and extracted data. Quality was assessed using the Modified Medical Education Research Study Quality Instrument and Cochrane Risk of Bias tools. A narrative synthesis was performed. Results Of 2367 records screened, 26 met inclusion criteria. Confidence in supporting others and mental health literacy showed the most consistent improvements following MHFA training, whereas evidence for changes in stigma was mixed. Peer support, self-care and student well-being were infrequently examined, although qualitative data suggested that MHFA had improved openness to help-seeking. Conclusions MHFA shows promise in enhancing mental health literacy, confidence and intentions, and in reducing stigma, particularly when supplemented with experiential learning. HCP students may benefit from tailoring of such courses to their specific needs, fostering a culture of peer support, enhancing well-being and introducing basic concepts in mental health.

Quality Indicators to Evaluate Discharge Planning Processes in Mental Healthcare Settings: A Modified Delphi Survey of a Multidisciplinary Expert Panel.

AIM To achieve expert consensus on quality indicators for evaluating discharge planning processes in inpatient mental healthcare settings. This study is part of a larger multiphase project to develop an instrument to measure and evaluate the quality of discharge planning in mental healthcare. DESIGN A modified two-round Delphi study. METHODS A province-wide multidisciplinary expert panel comprising nurses, social workers, occupational therapists and psychiatrists across Ontario, Canada completed two online survey rounds. Panel members assessed 79 preliminary indicators identified from a prior concept analysis and focus groups, rated the importance of each indicator and provided narrative feedback to support indicator refinement. RESULTS In Round 1, 37 panellists evaluated 79 quality indicators. Seventy-four indicators achieved the predefined consensus threshold; however, further review of panel feedback resulted in the removal, consolidation and refinement of indicators due to conceptual overlap, redundancy, or feasibility concerns. Thus, fifty-three indicators were retained following Round 1. In Round 2, 36 panellists reviewed the revised indicator set and evaluated three new candidate indicators. Two additional indicators achieved consensus and one indicator was reinstated, resulting in a final set of 56 indicators across seven domains: Comprehensive Needs Assessment (n = 14), Information Gathering and Synthesis (n = 3), Patient Capacity Assessment (n = 4), Collaborative, Patient-Centred Care (n = 14), Resource Availability Management (n = 5), Care and Service Coordination (n = 7) and Discharge Plan (n = 9). CONCLUSION A multidisciplinary expert panel reached consensus on 56 quality indicators evaluating discharge planning processes in inpatient mental healthcare settings. The findings provide an evidence-informed foundation for future development and testing of measurement approaches to evaluate discharge planning quality in mental healthcare. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE The discharge planning domains and quality indicators identified in this study may support future efforts to evaluate discharge planning processes, identify gaps in care and inform quality improvement initiatives aimed at supporting continuity of care and transitions from hospital to community settings. IMPACT This study addresses the lack of standardized quality indicators for evaluating discharge planning processes in inpatient mental healthcare settings. Through a two-round Delphi process, a multidisciplinary expert panel reached consensus on 56 indicators reflecting core components of discharge planning, including comprehensive needs assessment, patient capacity evaluation, coordination of community services and supports, housing-related planning and follow-up care. The study findings provide a foundation for future development and psychometric evaluation of a standardized measurement instrument to assess discharge planning quality. REPORTING METHOD This study is reported in accordance with the Guidance on Conducting and Reporting Delphi Studies (CREDES) recommendations. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.

Integrating MCDA and economic evaluation into a rule-based framework for hospital-level health technology assessment

ABSTRACT Introduction: Health Technology Assessment (HTA) supports evidence-based decision-making, but integrating economic evaluation within Multi-Criteria Decision Analysis (MCDA) remains challenging in hospital-level HTA. This study developed and preliminarily applied an integrated decision-support framework combining MCDA, economic evaluation, and contextualized evidence assessment to support adoption recommendations. Methods: A pragmatic digital framework integrated MCDA with Budget Impact Analysis (BIA) and Cost-Consequence Analysis (CCA) within a rule-based decision matrix. The economic module estimated cost per patient, incremental cost, budget impact, cost-consequence outputs, and, when appropriate, a supportive soft ICER. Evidence was assessed through structured critical appraisal, Summary of Findings tables, and outcome relevance, using a GRADE-informed approach mapped onto the predefined 0-1-3-5-7-9 MCDA evidence scale. Operational robustness was assessed through Failure Mode and Effects Analysis (FMEA) and self-audit controls. Results: In a case study comparing disposable and reusable continence care systems, the disposable option showed a lower MCDA score than the current standard (2.60 vs 4.80; ΔMCDA = -2.20) and a higher cost per patient (€1.00 vs €0.65; incremental cost €0.35). Although the 3-year budget impact was €2,634 and remained within the institutional affordability threshold, the technology was classified as dominated because it generated lower value at a higher cost. The final recommendation was not favorable for routine adoption. Conclusion: The framework supports transparent integration of MCDA and economic evaluation in hospital-level HTA and helps distinguish affordability from overall value. The case study suggests that technologies should not be adopted solely because their budget impact is acceptable when the value-risk profile is unfavorable. Further validation across technologies and settings is required.