Redefining Access to Mental Health Care Through Sustained Tele-mentoring: A Report of the Instant Collaborative Video Consultations with Primary Care Doctors

Abstract

Background:

Mentoring primary care doctors (PCDs) improves their competency and aids in integrating psychiatric care into primary care, especially in resource-constrained settings. This study evaluates the socio-demographic, diagnostic, and treatment profiling of instant collaborative video consultations (i-CVCs) and the diagnostic (domain-wise and disorder-wise) and treatment concordance of PCDs with tele-psychiatrists.

Methods:

This was a cross-sectional study that was a part of a digitally driven capacity-building program that provided tele-mentoring to PCDs via i-CVCs with tele-psychiatrists. A total of 382 i-CVCs conducted between November 2022 and July 2024 were analyzed for diagnostic and treatment profiling. Concordance between PCDs and tele-psychiatrists was assessed using Cohen’s kappa (n = 358).

Results:

Among the domain-wise diagnostic distribution by the tele-psychiatrist, the majority were classified as common mental disorders (CMDs) (47.7%), and among the disorder-wise distribution, the majority was depressive disorder (22.5%). The analysis revealed very good to good diagnostic agreement between PCDs and tele-psychiatrists across major domains: SUD (κ = 0.96), SMD (κ = 0.81), and CMD (κ = 0.75), all with p < .001. Disorder-wise concordance was highest for alcohol disorders (κ = 0.96). Treatment concordance also showed moderate agreement for all the major classes of psychotropic medications (κ = 0.5 to 0.6; p < .001), including antidepressants, antipsychotics, benzodiazepines, and nicotine replacement therapy. i-CVC support was primarily sought for diagnostic/treatment confirmation and clarification on dosage strategies.

Conclusions:

i-CVCs facilitate real-time learning and improve confidence in managing psychiatric disorders within live primary care clinics, demonstrating a scalable approach to address the treatment gap.

Key Messages:

• i-CVCs between PCDs and tele-psychiatrists were analyzed, focusing on diagnostic and treatment concordance in mental health care.

• i-CVCs showed good diagnostic agreement between PCDs and tele-psychiatrists and moderate agreement for psychotropic medications.

• i-CVCs enhance PCDs’ diagnostic and management skills, offering a scalable solution to address the mental health care gap.

Mentoring primary care doctors (PCDs) with an MBBS qualification in basic psychiatric skills is essential for improving their competencies. This supports the integration of mental health care into primary health settings and helps address the treatment gap. Studies indicate that a significant portion of patients in primary care settings have psychiatric issues, yet many PCDs feel inadequately prepared to address these concerns.^1,2 This is particularly problematic in low-resource settings, where access to specialized psychiatric care is limited, and PCDs often serve as the first point of contact for patients with mental health issues. ³ Innovative programs such as the Karnataka Telemedicine Mentoring and Monitoring Program demonstrate the potential of digital platforms to offer real-time feedback and ongoing support to PCDs, enhancing their ability to manage mental health conditions in practical settings. ⁴

This article presents an initiative for mental health capacity building using digital technology to train and mentor PCDs, community health officers, and field-level workers in primary care psychiatry through “A Pan India Digitally Driven Capacity Building Program to strengthen Primary Mental healthcare.” This program provided tele-mentoring through an instant collaborative video consultation (i-CVCs) module, allowing PCDs to engage live and instantly with specialists to provide consultations to their patients on-spot without any waiting time. i-CVCs are an effective tool in training PCDs, particularly in enhancing their skills in diagnosing and managing mental health conditions. i-CVCs offer real-time, real-world case-based collaborative learning, helping doctors refine their diagnostic and therapeutic approaches. Through i-CVCs, tele-psychiatrists guided PCDs at primary health centers (PHCs) to assess, diagnose, and manage patients with mental health conditions. In this study, “tele-psychiatrists” refers to general psychiatrists who provided teleconsultations remotely using video-based platforms. The teleconsultations followed a hub-and-spoke setup with the hub located at the NIMHANS Digital Academy under the psychiatry department of Psychiatry and the spokes being the PCDs and their patients at a live PHC clinic. Each consultation lasted 10–15 minutes, during which tele-psychiatrists provided guidance and recommendations to the PCDs. This study aimed to evaluate the effectiveness of an i-CVC tele-mentoring program in improving the mental health capacity of PCDs. The objectives of this study were to: (a) To describe the socio-demographic, diagnostic, and treatment profiles of patients presenting in i-CVCs, (b) To assess the diagnostic and treatment concordance between PCDs and tele-psychiatrists and analyze domain and disorder-wise diagnostic concordance, and (c) To evaluate key learning themes from i-CVCs.

Methods

This was a cross-sectional study conducted as a part of a digitally driven capacity-building program where PCDs were trained in primary care psychiatry for 12 hours in 6 weeks (Shah H et al., 2025, titled “A Pan India Digitally Driven Capacity Building Program to strengthen Primary Mental healthcare: Summary of its Implementation and Performance evaluation,” currently accepted for publication). The duration of each session was for two hours, and i-CVC support was provided to the PCDs during and after the training. They were trained using Clinical Schedules for Primary Care Psychiatry: Version 2.4 (CSP 2.4), which is a point-of-care manual explicitly designed for PCDs containing guidelines for screening, early diagnosis, referral, first-line prescription templates, and routine follow-ups of adult patients with psychiatric disorders at outpatient settings of PHCs. ⁵ The CSP was chosen because it helps PCDs establish psychiatric caseness and achieve a broader diagnosis in three domains: common mental disorders (CMDs), severe mental disorders (SMDs), and substance use disorders (SUDs). It is validated and has relatively high sensitivity with reasonably high specificity. ⁶

The i-CVCs were delivered by the tele-psychiatrists of this program for the PCDs working in the primary health centers of the following states of India: Karnataka, Maharashtra, Telangana, Uttarakhand, and Goa. All PCDs who underwent the structured training were eligible to initiate i-CVCs. Requests for i-CVCs were received through district-specific groups created on e-messaging platforms with PCDs across various states. Tele-psychiatrists then promptly contacted the PCDs and conducted consultations using video-based platforms. Verbal informed consent was obtained from the patient or caregiver before initiating each i-CVC. This approach was chosen due to the live clinical nature of the consultation at PHCs, where obtaining written consent would have disrupted the patient care process. In the case of patients under 18 years of age, verbal consent was obtained from a parent/caregiver, and assent was obtained from the child/adolescent wherever developmentally appropriate. This consent procedure was reviewed and approved by the Institutional Ethics Committee (No: NIMHANS/43rd IEC(BEH.SC.DIV.)/2023), letter dated 08.12.2023. Furthermore, the process aligns with the Indian Council of Medical Research’s National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (2017) permits verbal/oral consent in public health research under specific conditions (Section 8.4.3). ⁷

Initially, the PCDs were asked to provide the patient’s chief complaints, brief history, preliminary diagnosis, and initial management plan. The patient was then briefly engaged to confirm the diagnosis and address any uncertainties the PCD had, especially regarding diagnostic dilemmas or potential treatment adjustments. Treatment recommendations were made based on the psychiatric medications available at the PHC, following the CSP module. After each i-CVC, tele-psychiatrists completed a designated format, recording details such as the PCD’s name, PHC location, and state. Additionally, basic demographic information of the patient—age, gender, marital status, and clinical variables like chief complaints, PCD diagnosis and treatment, tele-psychiatrist diagnosis and treatment, and key learning points of that i-CVC were documented.

The i-CVCs received from November 2022 to July 2024 (20 months) were included in the study, and a cross-sectional data analysis was done. A total of 413 i-CVCs were received during this study period. Cases that could not be diagnosed by the tele-psychiatrist (n = 31) were excluded from this study. These 31 cases were undiagnosed due to inadequate clinical history, often because attendants were unavailable or further investigations were needed. They were excluded to avoid unwarranted clinical categorization and to ensure diagnostic accuracy. So, a total of 382 i-CVCs were included for diagnostic and treatment profiling. No missing data were present for the key variables, and all analyses were based on the full dataset. For diagnostic and treatment profiling and distribution of learning themes, follow-up cases were not excluded to reflect the full spectrum of clinical engagement during the program.

In contrast, for diagnostic and treatment concordance analysis between PCDs and tele-psychiatrists, only first-time consultations were considered as PCDs would already be aware of the initial diagnosis and treatment suggested by the tele-psychiatrist for follow-up cases. Hence, follow-up cases (n = 24) were excluded from the concordance analysis (n = 358) to avoid potential bias. These details were depicted in Figure 1. The diagnostic concordance was measured across three domains, viz: (a) CMDs include the diagnoses of depressive disorder, anxiety disorder, somatization disorder, and mixed disorder, (b) SMDs include all psychotic disorders, and (c) Alcohol use disorders and tobacco addiction, both grouped as SUDs. In concordance analysis, we studied both domain-wise and disorder-wise concordance rates. For treatment concordance, only the primary class of drug was taken instead of a specific drug, that is, Antidepressants for CMDs, Antipsychotics for SMDs, Benzodiazepines, and NRT for SUDs.

Figure 1.

Flow Chart Showing the Inclusion and Exclusion of i-CVCs for the Diagnostic and Treatment Profiling and Concordance.

For diagnostic and treatment profiling and distribution of learning themes, follow-up cases were not excluded to reflect the full spectrum of clinical engagement during the program whereas for diagnostic and treatment concordance analysis between primary care doctors and tele-psychiatrists, only first-time consultations were considered as primary care doctors would already be aware of the initial diagnosis and treatment suggested by the tele-psychiatrist for follow-up cases. Hence, follow-up cases (n = 24) were excluded from the concordance analysis (n = 358) to avoid potential bias.

Statistical Analysis

The statistical analyses were done using IBM SPSS Statistics 29.0. The agreement between PCDs and psychiatrists was measured using Cohen’s d and kappa. The κ value can be interpreted as follows: <0.20: Poor agreement; 0.21–0.40: Fair agreement; 0.41–0.60: Moderate agreement; 0.61–0.80: Good agreement; 0.81–1.00: Very good agreement.

Results

Table 1 presents the socio-demographic profile of the patients who received i-CVCs (n = 382). The majority were males (52.6%) and were married (72%). In the age-wise distribution, the majority were between 18 and 60 years old (87.2%). Table 2 outlines the domain-wise and disorder-wise diagnostic distribution as determined by the tele-psychiatrist. The majority were CMDs, comprising 47.7% of cases. In disorder-wise diagnostic distribution, among CSP diagnoses, the majority were depressive disorders (22.5%). Table 3 describes the type of treatment provided to patients. Antidepressants were the most common, prescribed to 51.3% of patients, followed by benzodiazepines (18.1%). Table 4 outlines the learning themes from i-CVCs. The majority of them have initiated i-CVC to confirm their diagnosis and choice of treatment (35.1%). Table 5 outlines domain-wise diagnostic concordance between PCDs and tele-psychiatrists. The agreement for SUDs and SMDs was very good (0.96 and 0.81, respectively and for CMDs, there was good agreement (0.75), all with significant p values (<.001). Disorder-wise diagnostic concordance among the clinical sample was highest for Alcohol addiction (0.96), indicating very good agreement. Table 6 presents treatment concordance across various medications. Antidepressants, antipsychotics, benzodiazepines, NRT (Nicotine replacement therapy), and counseling showed moderate concordance, with p values indicating statistical significance (<.001).

Table 1.

Socio-demographic Profile of the i-CVCs (n = 382).

Socio-demographic Profile		N (%)
Gender	Male	201 (52.6)
	Female	181 (47.4)
Marital status	Married	275 (72)
	Unmarried	82 (21.5)
	Separated/widow/divorced	25 (6.5)
Age	Less than 18 years	20 (5.2)
	18–60 years	333 (87.2)
	More than 60 years	29 (7.6)

Table 2.

Domain-wise and Disorder-wise Diagnostic Distribution by Tele-psychiatrist (n = 382).

CSP Diagnosis	N (%)
Substance use disorders (SUDs)	75 (19.6)
Tobacco addiction	7 (1.8)
Alcohol disorders	65 (17)
Both alcohol disorders and tobacco addiction	3 (0.8)
Severe mental disorders (SMDs)/psychosis	53 (13.9)
Common mental disorders (CMDs)	182 (47.7)
Depressive disorder	86 (22.5)
Generalized anxiety disorder	46 (12.1)
Panic disorder	8 (2.1)
Mixed disorder	14 (3.7)
Somatization disorder	28 (7.3)
SMD, SUD	5 (1.3)
Non-CSP diagnosis	67 (17.5)

CSP: Clinical schedules for primary care psychiatry.

Bolded rows represent major diagnostic categories (domains) which sum up to 100%.

Subcategories listed below each represent specific disorders within that domain.

The category “SMD + SUD” indicates dual diagnosis cases and is listed separately to avoid overlap in domain-wise totals.

Table 3.

Type of Treatment Received by the Clinical Sample (n = 382).

Type of Treatment Received Predominantly	N (%)
Antidepressants	196 (51.3)
Antipsychotics	49 (12.8)
Benzodiazepines	69 (18.1)
Nicotine replacement therapy	6 (1.6)
Mood stabilizers	3 (0.8)
Counselling/psychoeducation	12 (3.1)
Referral	43 (11.2)
Other drugs	3 (0.8)
Both, antipsychotic + antidepressant	1 (0.3)

Table 4.

Learning Themes from the i-CVCs (n = 382).

Particulars	N (%)
For confirmation of diagnosis and treatment	135 (35.3)
Clarification regarding medication or dosage	84 (22)
Clarification regarding both diagnosis and treatment	56 (14.7)
Management in special population-adolescent/pregnancy/post-partum/elderly	25 (6.5)
Follow-up management	24 (6.3)
Non-pharmacological management	14 (3.7)
Identification of red flags-suicidality/deliberate self-harm	12 (3.1)
Referrals (IP care for uncooperative patients/IQ assessments/other specialists)	13 (3.4)
Importance of investigations/ruling out medical causes	12 (3.1)
Importance of detailed history-taking	4 (1.1)
Management of comorbidities	3 (0.8)

Table 5.

Diagnostic Concordance Among the Sample (n = 358).

		Domain/Disorder	Kappa Value	p	Agreement
I		SUDs	0.96	<.001	Very good
	a	Tobacco addiction	0.95	<.001	Very good
	b	Alcohol disorders	0.96	<.001	Very good
II		SMDs/psychosis	0.81	<.001	Very good
III		CMDs	0.75	<.001	Good
	a	Depressive disorder	0.68	<.001	Good
	b	Anxiety disorder	0.62	<.001	Good
	c	Panic disorder	0.56	<.001	Moderate
	d	Somatization disorder	0.73	<.001	Good
	e	Mixed disorder	0.59	<.001	Moderate

SUDs: substance use disorders, SMDs: severe mental disorders, CMDs: common mental disorders.

Table 6.

Treatment Concordance Among the Sample (n = 358).

Domain	Kappa Value	p	Agreement
Antidepressants	0.46	<.001	Moderate
Antipsychotics	0.59	<.001	Moderate
Benzodiazepines	0.58	<.001	Moderate
Nicotine replacement therapy (NRT)	0.50	<.001	Moderate
Counselling	0.49	<.001	Moderate

Discussion

The findings of this study highlight the effectiveness of capacity-building programs in empowering PCDs, demonstrating that i-CVCs serve as a valuable extended hand-holding tool, particularly in resource-limited settings, and can be easily integrated into primary care practice. With a substantial portion of patients diagnosed with CMDs, this study aligns with previous findings indicating that CMDs are the most common psychiatric cases encountered in primary care settings, particularly in lower- and middle-income countries (LMICs). ⁸

The program’s emphasis on diagnostic and management skills enhanced PCDs’ capacity to screen and identify symptoms of SUDs, SMDs, and CMDs with greater accuracy, as indicated by the level of agreement of PCD with tele-psychiatrists in the domain-wise analysis (kappa values are 0.96,0.81, and 0.75, respectively). Early detection and consistent management in primary care can be transformative and lessen the burden on specialized mental health services, especially in regions with limited access to psychiatric specialists.

In disorder-wise diagnosis, good concordance was observed for disorders such as depression, generalized anxiety disorder, and somatization disorder, and moderate concordance for panic disorder and mixed disorder, which indicates that identification of these disorders at the level of PCD was more difficult than SMDs and SUDs. It implies that the subcategorization of CMDs at the primary care level can be difficult due to overlapping symptoms. However, the idea behind CSP is a transdiagnostic cluster approach where the focus can be on the identification of these disorders as CMDs, as this was sufficient to initiate first-line treatment with antidepressants by the PCD. ⁹ This also suggests the need for sustained mentoring where a PCD can learn in real-time through i-CVCs, as each case can have a varied presentation of symptoms.

The concordance rates for depression were impacted by the fact that tele-psychiatrists often diagnosed conditions like adjustment disorder and dysthymia. However, as these diagnoses were not explicitly covered in the CSP manual, PCDs misidentified them as depression. The concordance rates for somatization disorder were also affected due to the omission of the underlying physical diagnosis by the PCD. Although PCDs may not consistently differentiate among subtypes of CMDs such as anxiety, depression, or somatization, they tend to prescribe the primary treatment—typically antidepressants—which can effectively address symptoms and help alleviate the mental health burden. It may be noted here that even in tertiary settings, antidepressants as a class are the first line of treatment for CMDs.

The treatment concordance analysis revealed a moderate agreement in the prescribing practices of antidepressants, antipsychotics, benzodiazepines, and Nicotine replacement therapy. This shows that despite identifying the problems correctly, PCDs may be hesitant to prescribe psychotropic medication. This dissonance between diagnostic and prescription competence is a ground-level reality that needs to be addressed for a meaningful translation of the acquired skill. i-CVCs are particularly useful in this context, where doubts about starting a medication can be cleared, and as time progresses, their confidence improves, and they could become self-reliant. Furthermore, there is a need for sustained mentoring to build confidence in the choice of the drug, choosing the appropriate dosage, and understanding of potential side effects. This innovative approach can lead to improved patient outcomes at the primary care level.

The learning themes from the i-CVC module also suggest that the majority of the PCDs utilized i-CVC to confirm the appropriateness of their clinical decisions. It implied that modules like i-CVCs help them boost their confidence after their initial training in primary care psychiatry. It was noted that a considerable proportion of i-CVCs focused on clarifying treatment-related issues, including questions about the choice of medication, optimal dosing, and side effects. The study also identifies specific challenges faced by PCDs, such as management in child and adolescent cases, geriatric cases, and neurological cases (as described in Table 4), providing insights about the unmet needs.

To date, there are few studies where the diagnostic and treatment profiling of i-CVCs have been done, and the effectiveness of the training program has been seen through overall concordance. ¹⁰ This is the first study where domain and disorder-specific concordance analysis was done, and we were able to see the areas where PCDs are facing difficulty and the need for sustained mentoring.

While the i-CVC model demonstrates strong potential, its large-scale implementation will require addressing several key challenges. Technological limitations such as unstable internet connectivity and inconsistent access to digital platforms remain significant barriers in many PHCs. Standardizing training and sustained mentoring for PCDs across diverse healthcare systems is also crucial to ensure consistent quality of care. Additionally, the availability of essential psychotropic medications at PHCs must be ensured to enable the implementation of recommended treatment plans. This i-CVC model can also be scaled up by establishing digital academies at Tele MANAS mentoring institutes that can offer training and sustained mentoring to PCDs. A hub-and-spoke model can be employed, where the “hub” (Mentoring institute) provides central coordination and psychiatric expertise, and the “spokes” (PHCs and PCDs) function as decentralized points of care. A tiered mentoring system can be established to ensure ongoing, accessible support for PCDs beyond initial training. This should include:

Tier 1: Real-time i-CVC support through mentoring institutes of Tele MANAS with dedicated tele-psychiatrists.

Tier 2: Referral support from district-level DMHP psychiatrists, district hospitals, or medical colleges for complex cases when required.

Tier 3: Periodic case-based learning reviews, supervision, and feedback loops through regional digital academies.

Importantly, this model can be seamlessly integrated with the existing TeleMANAS initiative, as dedicated psychiatrists are already present at the mentoring institutes. These psychiatrists can also handle i-CVCs initiated by PCDs, thereby optimizing existing human resources and infrastructure.

In summary, the i-CVC model validates clinical decisions, strengthens skills, transforms skills into practice, and enables doctors to manage mental health conditions effectively, ultimately improving patient outcomes. Innovative initiatives like the i-CVC model, when backed by a structured training program, have the potential to significantly improve diagnostic accuracy, treatment outcomes, and overall quality of care.^10–17 This model also showed that even in a busy PHC setting, when a specialist is available for support through tele-mentoring, PCDs tried to utilize the opportunity of doing an i-CVC and clear their doubts about diagnosis or management. This approach promotes diagnostic and treatment consistency and shows promise as a scalable and cost-effective model to address the unmet mental healthcare needs in India by leveraging digital platforms.

Future studies can explore the duration required for extended hand-holding through i-CVCs for long-term retention of clinical skills and further investigate additional interventions that may enhance diagnostic and treatment concordance. The inclusion of multiple Indian states (Karnataka, Maharashtra, Telangana, Uttarakhand, and Goa) in this study enhances the potential generalizability of our findings. These states represent a range of geographical locations, healthcare infrastructures, and population demographics within India. However, it is crucial to consider the specific cultural and socioeconomic factors when applying these results to other settings, particularly in LMICs.

Strengths

This is the first published study describing i-CVCs with detailed domain- and disorder-specific concordance analysis, identifying areas requiring further support. The use of a validated point-of-care training manual (“Clinical Schedules for Primary Care Psychiatry 2.4”) ensured standardization in training and treatment protocols.

Limitations

Although no participants declined participation due to the verbal consent process, we acknowledge the potential for selection bias in different settings. However, as the study involved multiple interfaces (the researcher, the PCD, and the patient/caregiver), it is practically difficult for the written informed consent process in busy primary care settings, where the essence is for quick completion of the consultation. Also, the PCDs would ask for i-CVCs to improve the quality of care and for their learning. It is not a case where patients are directly recruited solely for research purposes. Although many i-CVCs were initiated by PCDs for routine cases to seek validation for their clinical decisions, the possibility of selection bias cannot be excluded due to the voluntary nature of case selection by the PCDs. While the study demonstrates success in upscaling PCDs’ competence/skills, it does not explore patient outcomes directly. As this was a cross-sectional study, it captures a snapshot of diagnostic and treatment concordance at one point in time. Therefore, causal inferences regarding the impact of i-CVCs on long-term skill enhancement or patient outcomes cannot be made. To avoid overestimating agreement, follow-up i-CVC cases were excluded from the concordance analysis since PCDs would already know prior tele-psychiatrist inputs. However, this limit assessing the long-term impact of i-CVCs on PCDs’ performance or improvement in their diagnostic and treatment skills.

Future Recommendations

Future studies should assess the long-term outcomes of i-CVCs, including sustained improvements in diagnostic and treatment skills among PCDs and clinical outcomes for patients. There is a need to integrate advanced training modules for specific areas, such as child and adolescent, neurological, and geriatric conditions, into the CSP manual.

Conclusions

These findings demonstrate that i-CVCs serve as a crucial extended hand-holding tool, particularly in resource-limited settings, and can effectively address the concerns of the PCDs through real-time feedback and sustained mentoring.

Supplemental Material

Supplemental material for this article is available online.