Proactive integrated mental health care (PIMHC) model for hospitalized surgical patients: Development and feasibility study

INTRODUCTION

The prevalence of mental health disorders (MHDs) (e.g., depression and anxiety) is high among hospitalized surgical inpatients, ranging from 5% to 40%.^[1] The MHDs have a significant impact on surgical outcomes such as post-surgical pain, length of hospital stay, complications, readmissions, and mortality.^[2] It also leads to decreased wound healing and quality of care.^[2] Despite extensive research into the relationship between surgical outcomes and MHDs, very few attempts have been made to develop an effective and feasible healthcare model that can address the MHDs among hospitalized surgical inpatients. A 2021 meta-analysis reports that eight trials were eligible for inclusion, all had methodological limitations, and were published more than 10 years ago.^[1] Furthermore, there is no conclusive evidence that the consultation–liaison (C-L) Psychiatry care model was superior to standard medical care alone.^[1] In addition, the meta-analysis did not include any prospective studies with robust study designs from India.

Despite a high prevalence of MHDs among hospital inpatients in India, referral rates for psychiatric services are much lower (0.15%–3.6%) compared to higher rates (around 10%) in Western countries.^[3,4] Currently, a clinician (psychiatrist) based, reactive C-L model is used to manage patients with MHDs in surgical wards in India. In India, the C-L services are on-call with a three-tier system (i.e., a team composed of faculty members, a senior resident, and a junior resident).^[5] The model has many limitations in terms of being reactive, consultee-based, having difficulties in communication, and lacking a multidisciplinary approach. Furthermore, the services provided under this model frequently result in medical or surgical care, which is often complicated, delayed, or compromised.^[3]

There is substantial evidence that timely and appropriate psychiatric care for surgical inpatients improves overall medical outcomes, reduces postoperative complications and readmissions, and shortens length of stay (LOS).^[4,6] Proactive C-L models have been developed and implemented in high-income countries (e.g., the United States of America) and have shown promising results.^[4,7] However, there is a striking lack of studies that develop or evaluate context-specific, proactive C-L models in low- and middle-income countries, including India. In the Indian context, the C-L model within surgical settings has historically received minimal attention in clinical service development, training, research, and innovation.^[8-10] This gap likely contributes to significant unmet mental health needs among surgical inpatients with co-occurring MHDs, leading to poorer outcomes and increased burden on patients, families, and health systems. Given this context, there is an urgent need to design and evaluate an indigenous, proactive, and feasible model that integrates mental health care in routine surgical care. The proposed study aims (1) to develop a proactive integrated mental health care (PIMHC) model for early screening, referral, and monitoring of MHDs among hospitalized surgical patients, and (2) to examine its feasibility in tertiary care teaching hospitals.

MATERIALS AND METHODS

This study was conducted between August 2024 and August 2025 in two sequential phases of 6 months each: (1) development of the PIMHC model for hospitalized surgical patients, and (2) feasibility study. After approval of the Institutional Ethics Committee (IEC), the two phases were implemented.

Phase 1: Development of PIMHC model

A multidisciplinary development team was constituted, comprising three surgeons, one clinical psychologist, three psychiatrists, one mental health nurse, and three surgical nurses, all with a minimum of 3 years of experience in mental health conditions and/or surgical health care delivery. Model development followed a structured, multi-step process [Figure 1], guided by an established framework.^[11-13] A literature review examined the different models and care components. A needs assessment using a knowledge– attitude survey and qualitative feedback from service users and providers assessed awareness, service gaps, and barriers to mental health care among surgical patients. Expert consultations evaluated clinical effectiveness, feasibility, and contextual fit of previously published models. These inputs informed iterative modifications in the PIMHC model, including modifications to its structure, sequencing, and intensity of care. The developed PIMHC model was evaluated for feasibility in phase 2 of the study.

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Figure 1: Methodological framework for development of the PIMHC model. PIMHC: Proactive integrated mental health care, C-L: Consultation–liaison

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RESULTS

Phase 1: Development of PIMHC model

The model adopts a stepped-care approach to screen admitted surgical patients for mental disorders, with initial nurse-led screening followed by referral to psychiatric services for patients who screen positive. It integrates evidence-based screening tools for early identification of mental disorders, protocols to ensure timely and appropriate support, and structured training modules for nursing staff. The training builds competencies in screening administration, risk identification, referral pathways, brief supportive communication, and clinical documentation, thereby strengthening frontline capacity to deliver integrated mental health care within routine clinical workflows.

Literature review

Key best practices identified included routine mental health screening by nursing staff and collaborative care with surgical teams.^[1,8] Adaptations were planned to suit the specific sociocultural and resource context of a tertiary care hospital in Northeast India.

Needs assessment

A total of 15 stakeholders, including psychiatrists, surgeons, nursing staff, hospital administrators, patients, and caregivers, were interviewed using semi-structured interview guides. There was a strong consensus on the need for integrating mental health care into surgical services, driven by reported increases in mental health conditions and the observed negative impact on surgical outcomes and LOS. The identified key barriers include limited awareness among surgical staff, time constraints in busy wards, stigma associated with mental health conditions, and the absence of structured protocols for assessment and referral. Proposed solutions included incorporating mental health screening into routine admission procedures, providing brief support directly within wards, and establishing clear, streamlined referral pathways to facilitate timely mental health intervention.

PIMHC model components

Based on inputs, the PIMHC model was designed with the following core elements [Figure 2 and Table 1]: (1) Proactive routine screening: Brief, validated screening tools are administered by nursing staff at the time of admission and are used for follow-up assessments during hospitalization and after discharge at 2 weeks, 4 weeks, and 6 weeks. The screening battery includes the alcohol, smoking and substance involvement screening test (ASSIST) for substance use and addiction;^[14] the mental health screening and counselling tool for field level workers of India (MERIT) for alcohol and tobacco use, anxiety, somatoform and depressive symptoms, mood and psychotic disorders, and selected medical conditions;^[15] Nursing delirium screening scale (NuDESC) for delirium^[16]; and general health questionnaire–28 (GHQ-28) for somatic symptoms, anxiety and insomnia, social dysfunction, and depression.^[17] The primary rationale for including these tools was to enable comprehensive risk mapping of patients admitted to surgical wards. (2) Assessment and interventions: Screen-positive patients receive further assessment by mental health professionals within 12 h, followed by appropriate interventions. (3) Collaborative care: Surgical teams maintain regular communication with the psychiatry team to ensure continuity of care for surgical patients with mental health conditions. (4) Capacity building: A structured 4–5-h training module is provided for surgical ward staff, focusing on mental health awareness, use of screening tools, and referral mechanisms. (5) Supervision and quality assurance: Weekly supervision meetings of surgery and psychiatry teams to monitor fidelity and troubleshoot challenges. (6) Workflow integration development: Detailed workflows outlining responsibilities, timelines, and documentation processes are developed to minimize disruption to surgical care, with SOPs finalized and approved by authorities. (7) Feasibility benchmarks: Based on expert consensus, pragmatic internal feasibility benchmarks were developed a priori. These benchmarks were based on findings from published studies^[18-20] and on clinical service constraints within a public tertiary care hospital setting of Northeast India. These criteria were intended solely as internal feasibility indicators and not as externally validated cutoffs. Consistent with pilot and feasibility study methodology, these benchmarks are exploratory in nature and were used to assess study processes rather than intervention effectiveness.

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Figure 2: PIMHC model for integrated mental health screening, care, and follow-up in surgical settings. PIMHC: Proactive integrated mental health care, SOPs: Standard operating procedures

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Table 1: Workflow of PIMHC model for Surgical Inpatients

Step	Process	Responsible Team/Personnel	Outcome
1	Admission to surgical wards	Surgical team	Patient admitted for surgical care
2	Proactive mental health screening (GHQ-28, MERIT, ASSIST, and Nu-DEC)	Nursing staff	Identification of mental health conditions
3a	Screened positive	Nursing staff	Referral initiated
3b	Screened negative	Nursing staff	No psychiatric referral required
4a	Psychiatric consultation and proactive periodic follow-up (as recommended) or at 2 weeks, 4 weeks, and 6 weeks	Psychiatry team	Integrated care during hospitalization
4b	Continue routine surgical care with follow up at 2 weeks, 4 weeks, and 6 weeks	Surgical team +Nursing staff	Ongoing standard care
5	Discharge from surgical wards	Surgical + Psychiatry teams (if indicated)	Discharge with psychiatric consultation where required

ASSIST: Alcohol, smoking, and substance involvement screening test, Nu-DEC: Nursing delirium screening scale

Feasibility study findings

Table 2 and Figure 3 present the feasibility outcomes of the PIMHC model. A total of 61 patients were initially assessed for eligibility. Of these, 4 patients were excluded, primarily due to not meeting inclusion criteria (n = 1), declining participation (n = 2), and withdrawal of consent before screening (n = 1). This resulted in 57 eligible participants (93.44%) who were enrolled in the screening process. Among the eligible participants (n = 57), 21 (36.84%) patients had incomplete screening forms. Within this group (n = 21), only one participant (4.76%) screened positive on the completed portion of the screening tools and was referred to psychiatry services. The incomplete screenings included partial completion of tools such as GHQ-28, MERIT, ASSIST, and Nu-DESC, with varying proportions completed across these scales. The main reasons for non-completion were the too lengthy form, language barriers in Northeast India, and the busy schedule of nursing staff and junior residents.

Table 2: Feasibility outcomes of the proactive integrated mental healthcare screening model.

Feasibility domain	Operational definition of feasibility criteria	Benchmark	Number of participants (n) /total sample (n)	Proportion / prevalence (95% confidence interval) (%)	Feasibility criteria
Recruitment	Proportion of assessed patients enrolled in screening	≥80% of eligible patients enrolled	57/61	93.4 (87.2–99.7)	Fulfilled
Screening completion	Proportion of enrolled patients completing all screening tools	≥60% complete screening	36/57	63.15 (50.7–75.7)	Fulfilled
Identification rate	Proportion of screen-positive cases among fully screened participants comparable to reported prevalence in similar hospital-based studies	Identification rate within expected range (15%–30%)	10/36	27.77 (12.2–42.4)	Comparable to the reported prevalence
Referral uptake	Proportion of screen-positive patients completing psychiatric consultation	≥80% referral completion	10/10	100 (69.2–100)	Fulfilled
Follow-up (screen-positive) at 2 weeks	Proportion of screen-positive patients completing 2-week follow-up	≥30% follow-up	4/10	40 (12.1–73.8)*	Fulfilled
Follow-up (screen-negative) at 2 weeks	Proportion of screen-negative patients completing 2-week follow-up	≥30% follow-up	6/26	23.07 (9.1–43.3)*	Not Fulfilled
Follow-up (screen-positive) at 4 and 6 weeks	Proportion of screen-positive patients completing 4 and 6-week follow-up	≥30% follow-up	3/10	30 (6.7–65.2)*	Fulfilled
Follow-up (screen-negative) at 4 and 6 weeks	Proportion of screen-negative patients completing 4 and 6-week follow-up	≥30% follow-up	6/26	23.07 (9.1–43.3)*	Not Fulfilled
Time for completion	Average time required to complete screening	≤15 min	10–15 min	--	Fulfilled
Training	Proportion of surgical ward staff attending training workshops	≥70% staff trained	~80% attendance	--	Fulfilled
Implementation monitoring	Planned weekly implementation review meetings, conducted with action points	≥70% of planned meeting	40% of planned meeting	--	Not fulfilled (suggested to shift a fortnight meeting)
Acceptability (staff and patients)	Willingness to participate in screening, referral, and consultation	Low refusal and full referral uptake	Minimal refusals; 100% referral uptake	--	Fulfilled
Implementation challenges	Barriers affecting screening and follow-up	—	Lengthy tools, language barriers, and staff workload	--	Identified

CI: Confidence interval, *: Exact-Clopper-Pearson confidence interval

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Figure 3: PIMHC model-enrolment, screening, and follow-up pathways. PIMHC: Proactive integrated mental health care, NuDEC: Nursing delirium screening scale, ASSIST: Alcohol, smoking and substance involvement screening test, MERIT: Mental Health Screening and Counselling Tool, GHQ: General health questionnaire.

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Out of the 57 participants, 36 (63.15%) completed all four screening tools, namely GHQ-28, MERT, ASSIST, and NuDESC. Of the 36 participants, 10 (27.77%) screened positive, while 26 (72.22%) screened negative. All participants who screened positive (n = 10, 100%) subsequently completed a psychiatric consultation, indicating full uptake of referral among identified at-risk patients. At the 2-week follow-up, 4 of the 10 screen-positive participants (40%) completed the follow-up assessment. In contrast, among those who screened negative, only 6 of 26 participants (23.07%) completed the 2-week follow-up. Follow-up assessments at 4 and 6 weeks for participants who screened negative were not feasible to complete as planned, whereas follow-up for those who screened positive was feasible.

Table 3 presents the socio-demographic and clinical characteristics of the study participants. The mean age of the participants was 46.10 ± 14.06 years (95% CI: 42.50–49.70). Females constituted the majority of the sample (57.37%, n = 35), while males accounted for 42.62% (n = 26). In terms of surgical diagnoses, biliary diseases were the most common condition (19.67%, n = 12), followed by soft-tissue infections (14.75%, n = 9) and gastrointestinal diseases (13.11%, n = 8). The mean length of hospital stay was 5.36 ± 3.66 days (95% CI: 4.42–6.30 days). Average screening time length was 10–15 min, which was manageable within the clinical workflow. Training workshops were attended by 80% of surgical ward staff, who demonstrated improved knowledge on post-training assessments.

Table 3: Socio-demographic and clinical characteristics of participants (n=61).

Variable	Mean±standard deviation (95% confidence interval), n (%)
Age (years)	46.10±14.06 (42.50–49.70)
Gender
Male	26 (42.62)
Female	35 (57.37)
Surgical diagnosis
Biliary diseases	12 (19.67)
Pancreatic disease	3 (4.91)
Gastrointestinal disease	8 (13.11)
Hernia	6 (9.83)
Breast disease	4 (6.55)
Vascular disease	6 (9.83)
Genital disease	5 (8.19)
Soft tissue infection	9 (14.75)
Soft tissue swelling	3 (4.91)
Trauma	4 (6.55)
Chest infections	1 (1.63)
Medical comorbidities (hypertension, diabetes, thyroid diseases)	21 (34.42)
Length of hospital stay (days)	5.361±3.661 (4.423–6.298)

The implementation monitoring criterion (≥70% of planned meetings) was achieved at only 40%, indicating a failure to meet this benchmark. Furthermore, the 4- and 6-week follow-up target (30%) was met among screen-positive participants (30%, n = 3/10) but remained below the threshold for screen-negative participants. These findings identify follow-up and implementation monitoring as the primary implementation challenges. Screening identified 27.77% of patients with common mental conditions. The present study was not designed or powered to assess effectiveness, and therefore, no conclusions regarding symptom change (e.g., depression, anxiety) can be drawn. However, patients expressed appreciation for being asked about their mental health during routine care. Caregivers valued the information provided and their involvement in care plans, noting improved communication with healthcare teams. Furthermore, nursing staff acknowledged the importance of integrating mental health care while highlighting workload and time constraints, suggesting further streamlining of the PIMHC model.

DISCUSSION

In India, psychiatric care among surgical or medical patients commonly follows a reactive C–L model, where psychiatric consultations are sought by primary clinicians only after active or overt mental health concerns are identified. The symptom-driven nature of the reactive model limits early detection, leads to delays in the delivery of mental health care, and prolongs hospitalization.^[5,8] With this study, we attempted to develop a C–L model within a general hospital setting, addressing limitations of reactive C-L models in India. Our model adopts a nurse-led stepped-care approach, incorporating proactive follow-up during hospitalization and after discharge. Unlike models implemented in Western countries, our model addresses key contextual barriers such as constraints of mental health professionals and limited or no formal reactive or proactive C-L training among nursing staff in India.^[10] The nurse-led care models are both feasible to implement and effective in screening for mental health problems in hospital settings.^[21,22] Building on this evidence, our PIMHC model provides an important opportunity to integrate mental health into routine surgical care and to reduce the existing treatment gap.

Western countries (the United States of America, the United Kingdom) have developed proactive models, which involve the collaboration among the general practitioners, case managers, and psychiatrists through task sharing with measurement-based follow-ups. In such settings, proactive care includes routine screening for common mental disorders and stepped care treatment protocols for integrative care. In line with these principles, our approach sought to incorporate basic mental health screening into tertiary-care surgical settings using a stepped-care approach.

The enrolment rate (over 85%) among patients who were approached and assessed for eligibility reflects a feasible recruitment within a convenience sample. This also indicates that the inclusion criteria were appropriate and clearly communicated, as screening and enrolment procedures proceeded smoothly following initial staff training. Importantly, mental health screening was consistently completed within 24 h of admission, demonstrating that the process was feasible to integrate into routine clinical workflows without disrupting surgical care. Follow-up assessments and interventions were delivered by trained mental health professionals, supporting the feasibility of incorporating structured screening within inpatient settings. Together, these findings indicate that an organized, proactive mental healthcare model can be implemented in tertiary surgical settings, while also identifying practical areas (such as use of GHQ-28) that require strengthening in future scale-up efforts.

Our study findings suggest that implementation of the PIMHC model is feasible in terms of recruitment, screening completion, and referral uptake, and demonstrates potential for improving the identification of mental health issues among surgical patients in resource-constrained settings. In addition, the use of the GHQ-28 was found to be challenging. Replacing it with the patient health questionnaire-9 (PHQ-9) and the generalized anxiety disorder-7 (GAD-7) may improve the feasibility of follow-up. Furthermore, these two scales are recommended for use in Indian settings and demonstrate psychometric properties comparable to western countries.^[23,24]

The integration of mental health screening into busy surgical ward routines initially resulted in delays and incomplete assessments. These challenges were addressed by including the screening process within the routine nursing admission checklist, which improved consistency and timeliness. Coordination gaps between the surgical and mental health teams were also observed in the early phase; establishing weekly interdisciplinary meetings helped streamline communication, clarify roles, and enhance continuity of care. In addition, frequent staff turnover and rotating shifts created difficulties in maintaining uniform competency across providers. To address this, refresher sessions and flexible, on-demand training modules were developed, ensuring that new and existing staff could continuously update their skills.

C-L models developed in other countries are typically based in well-resourced academic or large health-system settings, which makes their adoption difficult in India. In resource-limited settings such as India, adoption of these models more likely results in modest effect sizes. In contrast, our PIMHC model, developed on the principles of task sharing, has the potential to demonstrate comparable effectiveness, although that needs further evaluation. In addition, our model was found to be feasible with minimal supervision (i.e., periodic team meetings) and addresses the limitations related to weak monitoring systems in India.

Despite the overall feasibility of implementing the PIMHC model, follow-up assessments planned at 4 and 6 weeks were not feasible. This challenge reflects the difficulties in follow-up assessment once they are discharged. The primary reasons included patients residing in rural areas or in other Northeastern states, returning to work soon after discharge, or receiving postoperative care at multiple facilities, often in their home states. In addition, competing clinical priorities and limited staffing further constrained the team’s ability to complete scheduled follow-up assessments. However, this limitation highlights an important gap in continuity of care and underscores the need to explore alternative strategies, such as telephone-based follow-ups, integration with routine outpatient visits, or digital monitoring platforms, to ensure sustained engagement and outcome tracking in future studies.

CONCLUSION

The study has developed a structured PIMHC model that enables early screening, timely referral, and systematic monitoring of mental health conditions among hospitalized surgical patients. The model integrates mental health care within routine surgical inpatient care, demonstrating a practical approach to addressing the often-overlooked mental health needs of surgical inpatients. Further, the PIMHC model is feasible within surgical inpatient services. High levels of participation, along with successful implementation across key care pathways, suggest that the model can be integrated into routine clinical practice with further refinement in the screening process. Findings from this study will inform future effectiveness trials and support system-level mental health integration in surgical care in India.