Assessing the Effectiveness of Large Language Model (LLM)-Enabled Nurse Treatment Planning in 2 Indian Districts: A Pilot Study
Phase: NA
Conditions: Hypertension, Diabete Mellitus, Breathlessness, Fever
Sponsor: Sarah Nabia

 
 
 
Assessing the effectiveness of Large Language Model (LLM) - 
enabled nurse treatment planning in 2 Indian districts: A 
pilot study 
ETHICS REVIEW SUBMISSION – PROTOCOL, TOOLS & CONSENT 
FORMS 
PI: DR. ANUP AGARWAL 
Study Investigators: Ms. Sarah Nabia, Dr. Priyank Jain, Dr. Parthsarathi Mukherjee, Dr. Abhijit 
Chowdhury, Dr. Tushar Garg, Dr. Yogesh Jain, 
AUGUST 19, 2025 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
1 
 
I. 
Background and Rationale 
Large language model (LLM) based generative artificial intelligence (AI) is 
revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatments, 
and streamlining clinical decision-making. By offering evidence-based recommendations, 
AI empowers healthcare providers to develop more effective care plans. This capability is 
especially promising in low- and middle-income countries (LMICs), where urban–rural 
disparities in healthcare provider availability hinder access to quality care. 
In India—home to 1.43 billion people—a critical urban–rural divide persists. For 
example, the National Medical Commission reports that India’s overall doctor-population 
ratio is 1 per 843 persons (Update on Ratio Of Patients And Doctors Nurses, 2023), and 
the Economic Survey (2024-2025) stated that doctor density is 3.8 times higher in urban 
than in rural areas (Desk, 2025) . Using the most recent World Bank rural and urban 
population distribution, this implies 1 physician per 2,550 rural residents versus 1 per 670 
residents in urban India, which is more than a three-fold disparity. Although current 
initiatives, including availability of health and wellness centers,  infrastructure 
investments, telemedicine, and public–private partnerships in conjunction with 
longstanding community health worker programs like ASHA aim to mitigate these 
disparities, significant gaps remain. Leveraging AI tools to empower non-physician 
healthcare workers (e.g., nurses) to develop patient care plans represents a novel strategy 
to improve healthcare access and quality in underserved regions. 
LLMs have proven to be better than physicians in clinical reasoning and evidence based 
clinical solution when provided with clinical vignettes or diagnosis, respectively (Goh et 
al., 2024; Nori et al., 2025; Rao et al., 2023). However, LLMs have not been assessed for 
their ability to generate a clinical vignette via multiturn conversations when starting from 
a chief complaint. Some advances have been made in AI systems to conduct diagnostic 
dialogue with patient actors.  For instance, AMIE by Google has shown superior history-
taking and diagnostic accuracy in randomized, text-chat consultations with validated 
patient actors, yet it remains a research prototype that has not been tested in real-world 
clinical encounters.(Tu et al., 2025). To the best of our knowledge, LLMs have also not 
been assessed in real-life scenarios in resource-constrained settings.   
This research protocol outlines the plan for a within-subject, paired comparison study ( a 
type of crossover study design) to test the effectiveness of nurse - LLM paired solution in 
clinical reasoning and evidence-based clinical solutions in primary care settings when 
used for actual patients in a real-life encounter in a resource constrained LMIC context.  
The proposed pilot project will evaluate the effectiveness of the intervention for 
managing  Hypertension, Diabetes Mellitus (DM), musculoskeletal pain, breathlessness 
and fever. According to the ICMR-INDIAB study (2022), the weighted prevalence of 
DM and hypertension among adults > 20years was 11.4% and 35.5%, respectively 
(Anjana et al., 2023).  Another nation-wide study (POSEIDON study) estimated that 
fever (35.5%) was the most common symptom among patients presenting to a primary 
healthcare provider, more than half (50%) of all patients had respiratory symptoms, 
almost 10% patients had musculoskeletal symptoms (Salvi et al., 2015). High burden of 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
2 
 
these conditions and symptoms underscores the urgent need for scalable, high-quality 
care solutions in primary care settings. 
The research team will also study the user (nurse) and client (patient) experience of 
participating in an LLM empowered solution for bridging gaps in human resource for 
healthcare. The nurses’ experience will be captured through qualitative methods and the 
patient experience will be assessed via mixed methods.  
II. 
Objectives 
1. Assess the effectiveness of LLM-enabled, nurse-led treatment planning for select 
NCDs and symptoms compared to the standard of care. (primary objective) 
2. Measure the attitudes, enablers and barriers of patients towards a nurse + LLM care 
intervention in primary healthcare settings. (secondary objective I) 
3. Describe the attitudes and perceptions of nurses in using LLM tools in their clinical 
practice. (secondary objective II) 
III. 
Methodology 
This is a randomized, crossover study with embedded qualitative interviews for nurses 
and a cross-sectional exit survey for patients. Prior to patient enrolment, the LLM model 
and interface selection for the purpose of this study will be undertaken.  
A. 
LLM model selection 
Safety, clinical accuracy and adaptability to changing contexts are few of the many 
critical characteristics necessary for an LLM to be deployed in population-level clinical 
practice. Although there has been a rapid surge in the number of LLMs that can provide 
insights into clinical conditions, the reliability, accuracy, and relevance of these model 
outputs need to be systematically assessed before it can be deployed for programmatic 
use. HealthBench, by OpenAI is the largest known open-source benchmarking evidence 
available to compare various LLMs(Arora et al., 2025).  HealthBench was developed to 
evaluate LLMs on real-world healthcare tasks such as clinical decision support, symptom 
triage, and patient communication. Developed in collaboration with 262 physicians 
across 60 countries, HealthBench features 5,000 multi-turn, open-ended clinical scenarios 
assessed in 49 languages using 48,562 rubric-based criteria. It evaluates performance 
across five critical domains: scientific and clinical reasoning, patient safety, helpfulness, 
patient-centered care, and equitable communication.  
The March 2025 release of HealthBench evaluated 11 themes – which included global 
health as one. The criteria for assessing cases in the global health theme were: 
i. 
Adaptation to Resource Constraints 
Could the model recognize limited availability of tests, treatments, or specialists and 
adjust its recommendations accordingly? 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
3 
 
 
ii. 
Contextual Relevance 
Did the model align its responses with local epidemiology, common health conditions, 
and standard practices in LMICs? 
 
iii. 
Cultural and Linguistic Appropriateness 
Was the communication culturally sensitive and linguistically accessible across diverse 
populations? 
 
iv. 
Clinical Accuracy and Safety 
Were the medical recommendations both accurate and safe, especially in settings where 
clinical guidelines may differ? 
 
v. 
Recognition of Systemic Limitations 
Did the model identify when ideal care options might not be feasible and offer 
reasonable, safe alternatives? 
The LLM selection for this study will be anchored in evidence from the HealthBench. o3 
by OpenAI (60%), Grok 3 by X (54%) and Gemini 2.4 Pro by Google (52%) achieved 
similar overall performance scores. These models or others available in another 
HealthBench update before the study rollout will be vetted by a technology partner and 
by nurses (users) in a human-centered design workshop for practical considerations 
(Arora et al., 2025). 
B. 
Interface & usability testing 
We will conduct a structured human-centered design (HCD) workshop that integrates 
clinical simulation and usability evaluation. A HCD workshop is a structured, 
participatory process that places end users—such as patients, nurses, or physicians—at 
the center of solution development. It draws on methods from design thinking and 
behavioral science to understand users’ needs, generate ideas, and prototype solutions. 
Core principles include empathy, co-creation, iteration, and user testing. 
A group of MD-level physicians will first develop a set of realistic clinical cases on 
hypertension, diabetes mellitus, fever, breathlessness, and musculoskeletal pain 
representing common scenarios encountered in the target healthcare setting. These cases 
will be used to assess how nurses interact with different combinations of LLMs and user 
interfaces. A group of 10 to 15 practicing nurses will participate in a simulation exercise, 
where they solve these clinical cases using pre-selected LLM-interface combinations. 
The interfaces will vary in modality to reflect possible real-world deployments, including 
audio-only (spoken input/output) and multimodal interface (combining text and audio). 
Following each case, participants will independently rate their experience using a 
usability evaluation tool focusing on clarity, ease of use, responsiveness, and confidence 
in clinical decision-making. Structured debriefs and group discussions will follow to 
gather qualitative feedback on barriers, preferences, and perceived trustworthiness of the 
model-interface combinations. Findings from this exercise will inform the selection of the 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
4 
 
optimal LLM-interface combination for the implementation study and workflow 
integration strategies.  
C. 
Study design 
1. 
Primary objective: 
The intervention (nurse + LLM - patient encounter) and control (licensed physician - 
patient encounter) will be sequential. Each patient will participate in both the intervention 
and control arms, at the end of which separate treatment and follow-up plans are generated. 
This allows for direct comparisons within the same individual; thus, controlling for 
between-subject variability that may otherwise influence outcomes. Since the same 
patients are evaluated under both intervention and control, we can pair each nurse - LLM 
treatment outcome with the corresponding doctor-led outcome for that patient.  
Advantages: 
● Reduction in variability: Each patient acts as their own control, confounding 
variables like age, comorbidities and lifestyle factors are inherently balanced. 
● Increased statistical power: Paired design typically requires a smaller sample size 
to detect a given effect compared to parallel group designs because variability 
between study subjects is minimized making it a practical study design option for 
implementation science research.  
2. 
Secondary objective I 
A cross-sectional exit survey will assess patient experience following LLM-assisted, nurse-
led care using a brief tool developed from validated frameworks. Questions are adapted 
primarily from the WHO Health System Responsiveness domains (e.g., dignity, autonomy, 
etc), a globally recognized framework assessing dignity, communication, autonomy, and 
other aspects of patient experience in diverse health systems (Darby et al., 2000). 
Additional items are adapted from the Patient Satisfaction Questionnaire (PSQ-18), a 
widely used patient satisfaction tool developed by RAND Corporation (Marshall & Hays, 
1994). AI-specific questions on trust and comfort are based on recent literature evaluating 
patient perceptions of AI in healthcare (Nong & Platt, 2025). The tool will be pilot tested 
for clarity and cultural relevance to ensure appropriateness for the study population. To 
complement the quantitative findings from the exit survey, we will conduct in-depth 
interviews (IDIs) with a small, purposively selected sample of patients. These interviews 
will provide richer insights into patients' perceptions, trust, and comfort with the AI-
assisted nurse consultations, allowing us to explore contextual nuances and explanations 
that may not be captured through structured survey responses. 
3. 
Secondary objective II 
As part of the study, we will conduct IDIs with participating nurses to explore their 
experiences using the LLM during patient care. These interviews will provide qualitative 
insights into the usability, perceived accuracy, clinical relevance, and workflow 
integration of the LLM tool. Nurses will be asked about their comfort level, trust in the 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
5 
 
AI-generated recommendations, ease of interaction (e.g., via text or voice), and any 
challenges encountered during real-time use. Interviews will also explore suggestions for 
improving the tool's design and implementation. The IDIs will be conducted using a 
semi-structured guide, audio-recorded with consent, and thematically analyzed to inform 
future adaptation and scale-up of the intervention. 
IV. 
Study Setting 
The study will be implemented in 2 sites in the states of Chhattisgarh and West Bengal. 
 
Site 1: Nagari Village, Suri-I Block, Birbhum District, West Bengal 
Nagari is a rural village in Suri-I block of Birbhum district with a population of 1,959 
across 396 households as per the 2011 Census. The population is predominantly 
Bengali speaking, with 61.9% literacy (63.7% male; 60.3% female) (Nagari Village 
Population - Suri - I - Birbhum, West Bengal, n.d.). The village exhibits a balanced 
gender ratio (~1,102 females per 1,000 males) and includes substantial Scheduled 
Caste (49.5%) and Scheduled Tribe (27.8%) communities. Nagari relies on a nearby 
sub-center for primary care, with higher-tier facilities located ~10 km away in Suri 
(PHC and district hospital) . Most residents engage in agriculture or informal labor, 
with basic mobile access. This setting—with limited local clinical capacity and 
frontline nurse responsibility—is suitable for evaluating the usability and 
acceptability of nurse-delivered, AI-assisted clinical tools in a resource-constrained 
rural environment. 
 
  
 
Site 2: Ambikapur, Surguja District, Chhattisgarh 
Ambikapur is the district headquarters of Surguja (predominantly rural ~90%) in northern 
Chhattisgarh and serves as a center for a largely tribal and rural catchment population. It 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
6 
 
is a regional referral hub for over 1,700 villages, many with bare minimum health 
infrastructure.(Ambikapur Metropolitan Urban Region Population 2011-2025 Census, 
n.d.) The health system is supported by ASHAs, ANMs, and nurses, who play a vital role 
in frontline care. While Ambikapur has high literacy rates (~88%) many healthcare 
interactions still rely on in-person triage and verbal communication. Patients will be 
enrolled in 3 clinics  - namely, Amgasi village clinic, Argoti village clinic and Kuniya 
village clinic located 30 km, 60km and 50km from Ambikapur, respectively. These 
clinics are in rural and remote parts of the districts away from the district headquarters. 
 
V. 
Study Population 
A. 
Primary objective 
Inclusion criteria: 
Criteria 
Hypertensio
n 
Diabetes Mellitus 
Fever 
Musculoskel
etal pain 
Breathlessne
ss 
Age 
>18 years 
>18 years 
>18 years 
>18 years 
>18 years 
Duration of 
condition / 
symptom 
Known 
diagnosis 
Known diagnosis 
OR HbA1C > 6.4 
OR fasting blood 
sugar >126 OR 
PP >200 
 
 
 
Significanc
e of 
complaint 
If presented 
as chief 
complaint or 
in 
combination 
with DM 
If presented as 
chief complaint or 
in combination 
with 
Hypertension 
If presented 
as chief 
complaint 
and/or in 
combination 
with fever 
and 
If presented 
as chief 
complaint 
only and no 
evidence of 
fever 
If presented 
as chief 
complaint 
only and no 
evidence of 
fever 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
7 
 
breathlessne
ss 
 
Exclusion criteria: Individuals who don’t have the ability to provide informed consent 
due to lack of cognitive capacity (e.g. dementia, mental retardation, etc.).  
Acutely sick patients - Systolic Blood Pressure <90mmhg, SpO2 <92%, Respiratory rate 
>22/min 
Samples size 
We powered this single-session, counter-balanced, within-participant study to detect an 
absolute two-sided difference of ±3.5% in the consultation score between the intervention 
(nurse + LLM) and control (standard of care consultation by doctor). Assuming a within-
subject design variance term derived from Goh et al., an intraclass correlation (ICC) of 
0.10, two-sided α of 0.05, 80% power, and 5% dropout, the sample size across both sites 
for each clinical outcome is 336 (Goh et al., 2024). 
Site 
 
Clinical management 
(Hypertension & 
Diabetes Mellitus) 
Clinical reasoning 
(Fever, joint pain, 
breathlessness) 
Total 
Ambikapur 
168 
168 
336 
Birbhum 
168 
168 
336 
Total 
 
 
672 
Recruitment will be block-randomized equally between sites and sequences. 
1. 
Note: Sample size correction after pilot data 
A 30-patient internal pilot (15 per outcome) will be analyzed with a linear mixed-effects 
model that treats intervention, order (first vs second consultation), and site (study 
location) as fixed effects, and random intercepts will be included for each patient and for 
each doctor and nurse. We will extract the residual variance and random effect variances 
and use the simr package to re-estimate power for the planned sample size. The research 
team will inform the IRB if  adjustments > 10% to the sample is required.  
B. 
Secondary objective I 
Inclusion criteria: Only enrolled patients who can fill out / respond to a tablet-based exit 
survey in English / Hindi / Bengali.  
Sample size: All patients who have participated in both the intervention and control will 
be asked to complete the exit survey ~ 672 patients. A group of 30 to 40 patients (15 to 
20 in each site) who have completed the consultations in both study arms will be 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
8 
 
conveniently sampled to participate in the patient IDIs until we reach information 
saturation.  
C. 
Secondary objective II 
 Inclusion criteria: Nurses who have done at least 10 patient consults using the LLM tool.  
Sample size: 6 to 8 nurses who will do the consults across the 2 sites over the life of the 
project.  
VI. 
Study Procedures 
Recruitment: On arrival at the clinic, prospective participants will be screened by a nurse 
who will measure the blood pressure, blood sugar levels, temperature, and take an oral 
history of the reason for the visit. This is the existing history-taking procedure at the 
clinic sites. Patients who meet the inclusion criteria for any of the 2 conditions or 3 
symptoms will be offered the opportunity to enroll in the study.  
Informed consent: A research assistant will explain the objectives of the study, 
expectations from the participant – 1. to devote extra time to this visit as they would be 
seen by 2 providers (doctor and nurse + LLM) and 2. fill out an exit survey, and the 
incentive available for the participant if they fulfil the requirements of the study. The 
research assistant will also review other aspects of informed consent with the patient (e.g. 
confidentiality, data reporting, etc.). Patients who agree to participate in the study will 
sign an informed consent form. The processes for informed consent will be done in the 
local language.  
Randomization: Upon enrolment, the patient will be assigned to study sequence A or B, 
as per a computer-generated randomization sequence.  
● Sequence A (Nurse + LLM → Doctor): Participants receive the intervention first, 
followed by control. 
● Sequence B (Doctor → Nurse + LLM): Participants receive control first, followed 
by intervention. 
VII. 
Data Collection and Management 
Data Collection 
Quantitative: Data for quantitative analysis will be collected for primary objective and 
secondary objective I.  
Primary objective: The nurse + LLM – patient conversation will be audio recorded via a 
voice recording device. After taking the patient history, the nurse will enter the details 
into the LLM platform and carry out a multi-turn conversation with the LLM tool, 
seeking answers or clarifications from the patient, as required. Transcripts from the 
conversation and the LLM’s recommendation will be retrieved. Similarly, the doctor – 
patient conversations will be audio – recorded and a summary sheet with the differentials, 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
9 
 
diagnosis and recommendations will be filled out for each patient by the doctor. Outputs 
for each patient will be assigned a unique patient ID. Each patient will be associated with 
4 data sources – 1. Nurse- LLM -patient conversation transcript 2. Nurse-LLM generated 
prescription 3.  doctor-patient conversation transcript and 4. Doctor’s prescription.  
Secondary objective I: Upon completion of the nurse-LLM -patient consult, the 
participants will be asked to complete a short, 10-question exit survey about their 
experience and perceptions of the consult. The survey will be administered through a 
digital platform such as Qualtrics/ Survey Monkey and will be available in both Bengali 
and Hindi. We will not collect any personal identifiable details such as name or telephone 
number in the survey. The raw data will be retrieved in the form of .xls or .csv files and 
stored in a password protected cloud-storage drive.  
Note: Participant compensation: All patients enrolled in the study who complete all study 
activities (1 consult each with the doctor and nurse + LLM, and the patient exit survey) 
will receive a compensation of INR 250 in acknowledgment of their time spent in 
participating in the study.  
See figure 1 for study flow: 
 
Qualitative: In-depth interviews with a select group of patients will be conducted using a 
semi-structured interview guide. A convenient sample of respondents will be approached 
for participating in the patient IDIs. The interviewer will meet with the respondents in a 
semi-private location within 1 to 2 days of the clinic visit to complete the interview. Each 
patient IDI will last for 30 to 45 minutes. The patient IDIs will be conducted in either 
Bengali or Hindi, as per the respondent’s choice.  
Note: An additional INR 250 will be given as compensation to the patients who 
participate in the IDIs.  

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
10 
 
The IDIs with nurses will take 45 to 60 minutes and will be conducted in English or the 
participant’s preferred local language (Bengali / Hindi) in a mutually agreed semi-private 
setting.  The guide will focus on exploring questions about the comfort and usability of 
the AI tool, enablers and barriers of using an AI tool in a patient consult, willingness to 
integrate the tool in regular practice, and suggestions for improving the user experience.  
All interviews will be audio recorded and transcribed using software.  
Data Management 
1.  Data collection tools and formats 
 The study will generate both quantitative and qualitative data across its three 
objectives. Data sources will include: 
● Audio recordings and transcripts of nurse–LLM and doctor–patient consultations. 
 
● Prescriptions and treatment plans generated by the nurse–LLM interface and the 
attending doctor. 
 
● Responses from a structured, digital patient exit survey. 
 
● Transcripts of in-depth interviews (IDIs) with participating patients and nurses. 
All quantitative data will be collected using structured tools and stored in spreadsheet 
formats (.xls or .csv). Audio recordings will be transcribed using secure software, and 
qualitative interview transcripts will be exported as text documents. 
2. De-identification and confidentiality 
Each patient will be assigned a unique ID to link data across sources while 
ensuring anonymity. Personally identifiable information (PIIs) will not be 
recorded in transcripts, prescriptions, or survey responses. Transcription files will 
be reviewed and cleaned of any inadvertent identifiers. Names and other 
identifiers will be removed from audio and text data prior to analysis. 
3. Data storage and security 
All data will be stored in a secure, cloud-based storage system with restricted 
access. Access will be password protected and limited to designated members of 
the study team. Audio recordings, transcripts, and survey data will be stored in 
separate folders according to data type and collection site. Regular backups will 
be scheduled to prevent data loss. 
4. Data access and oversight 
Only authorized research team members will have access to identifiable data 
during initial processing. De-identified datasets will be shared with data analysts 
and physicians involved in scoring, with strict adherence to confidentiality 
agreements. All team members will be trained on data handling and privacy 
protocols in accordance with institutional ethics guidelines. 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
11 
 
5. Data retention and destruction 
 All data will be retained securely for a period of five years following the 
completion of the study, in line with institutional policy and national research 
standards. After this period, all data will be permanently deleted from storage 
systems. Audio recordings will be deleted after transcription and quality control 
are completed, unless retention is necessary for verification. 
6. Data sharing and publication 
 No individual-level data will be shared outside the research team. Findings will 
be reported in aggregate form, and any direct quotes from nurse interviews will 
only be included in publications or dissemination materials with explicit 
permission from the participants. Data sharing with external parties (e.g., journals 
or funders) will comply with institutional and legal guidelines, always ensuring 
participant anonymity. 
VIII. Outcomes and Measures 
A. 
Primary objective 
Two primary outcomes will be measured based on participant subgroups. The 
management reasoning outcome will be assessed among patients with a known 
diagnosis of hypertension or diabetes, while the diagnostic reasoning outcome will be 
evaluated among patients presenting with symptoms such as fever, breathlessness, or 
musculoskeletal pain. For both outcomes, each patient interaction—whether in the 
standard of care or intervention arm—will be scored by licensed physicians (evaluators) 
using pre-defined assessment criteria. Scores will range from 0% to 100%, representing 
the extent to which clinical decisions align with appropriate care standards. 
B. 
Secondary objective I 
The patient exit survey is designed to assess key implementation outcomes of the AI-
assisted, nurse-led care model from the patient perspective. The primary outcomes 
include perceived communication quality, trust and comfort with AI use, and overall 
acceptability and satisfaction. These will be measured using structured Likert-scale items 
adapted from validated tools, including the WHO Health System Responsiveness 
framework and the PSQ-18. Additional items explore willingness to receive similar care 
in the future, as a proxy for future acceptability. Quantitative responses will be analyzed 
using descriptive statistics and composite scores for key domains, while qualitative data 
will undergo thematic analysis to contextualize patient experience and expectations. The 
qualitative data will explore themes like those in the patient exit survey and will add 
contextual richness and nuance to the patients’ views.  
C. 
Secondary objective II 
The in-depth interviews with nurses are designed to assess their experience using an AI-
enabled clinical decision support tool in routine patient care. The interviews will explore 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
12 
 
themes related to the feasibility of using the tool (such as ease of use, impact on 
workflow, and access to required resources), and its acceptability (including trust in 
recommendations, comfort during use, and willingness to continue use in the future). 
Interviews will also examine practical challenges, such as digital literacy, internet 
connectivity, and preferred modes of interaction (text or voice).  
IX. 
Analysis Plan 
 
A. 
Primary objective  
Rubric-based grading: 
Transcripts and treatment plans from nurse and doctor consults for every patient will be 
graded using a modified version of the framework developed by Goh et al, which is a 19-
point scoring system. The scoring rubric used in Goh et al. assigns 1 point each for 3 
differential diagnosis (1*3 = 3 points), 0-2 points for the rationale for supporting each 
diagnosis (2*3 =6 points), and 0-2 points for rationale of opposing diagnosis (2*3 = 6 
points). It also assigns 1 point for final diagnosis (1*1 = 1 point), and 1 point each for 3 
next steps (1*3 = 3 points).  
 
Our study will adapt this grading rubric to the implementation context, keeping in mind 
the high footfall and general rush at the clinic, whereby a lengthy scoring scheme may 
add undue burden on the physician. We will use the scoring rubric without the rationale 
for supporting or opposing the diagnosis. For the 3 symptoms (fever, musculoskeletal 
pain and breathlessness), our scoring rubric will have 1 point each for 3 differential 
diagnosis (1*3 = 3 points), 2 points for final diagnosis (2*1 = 2 points), and 1 point each 
for 5 next steps (1*5 = 5 points). For each of the 5 next steps, negative 1 point will be 
assigned for harmful steps, 0 for inconsequential step and 1 positive points for correct 
step. The maximum possible score for the symptoms is 10 points.  
 
For the 2 known diagnoses (hypertension and DM), we have a 7-point scoring system. 
Maximum 2 points will be awarded for review of data (2*1 = 2 points). The other 
possible scores are 0 and 1. Maximum 2 points will be awarded for screening of 
complications (2*1 = 0). The other possible scores for this criterion are 0 and 1. 1 point 
each will be awarded for medication compliance assessment (1*1 = 1 point), counselling 
provision (1*1 = 1 point), and treatment plan (1*1 = 1 point). Negative 1 point will be 
awarded each for improper counseling and inadequate treatment plan.  
 
Each transcript and treatment plan will be graded by 2 assessors, who will be physicians 
with an MD degree in Internal medicine and have at least 7 years of clinical practice 
experience. The transcripts and treatment plan will be randomly assigned to the graders, 
and they will be blinded to each other’s assessments.  
 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
13 
 
The primary analysis will use the same mixed-effects model on all randomized patients 
with score for both intervention and SoC consultations. The null hypothesis 
𝐻!: 𝜇{#$%&'()*} − 𝜇{,-./-%} =  0 
will be tested with a two-sided 95% confidence interval. We will report the point 
estimate, CI, P-value, and intraclass correlations. Sensitivity analyses will repeat the 
model without provider random effects and with robust standard errors. 
B. 
Secondary objective I 
The analysis of the patient exit survey will focus on quantitative outcomes derived from 
Likert scale and categorical items grouped into predefined thematic domains: 
communication and understanding (Q1–3), trust and comfort with AI use (Q4–5), and 
respect and satisfaction (Q6–8). Descriptive statistics will be used to report frequencies, 
percentages, means, and standard deviations for each item. Where appropriate, composite 
domain scores will be calculated by averaging responses within each domain, and 
internal consistency will be assessed using Cronbach’s alpha. Responses to yes/no and 
categorical questions (e.g., willingness to receive similar care again) will be summarized 
using proportions. See the survey tool for more details on the questions. For the patient 
qualitative data, we will use an abductive approach for coding the transcripts using 
Dedoose, a collaborative qualitative analysis software(Abductive Coding in Qualitative 
Research, 2025). A codebook will be initially developed based on a priori assumptions 
and will be further refined after inductively coding three transcripts. This finalized 
codebook will then be applied to all transcripts for coding and analysis.  
 
C. 
Secondary objective II 
The analysis for the IDIs with nurses will follow the same approach as described above 
for the IDIs with patients. The initial codebook will be based on a priori assumptions for 
the interviews with nurses and iteratively refined through inductive coding.  
X. 
Ethical Considerations 
Informed Consent 
Written informed consent will be obtained from all participants before enrollment. A 
trained research assistant will explain the objectives of the study, study procedures, the 
expectations from the participant and the financial reimbursement to be offered as a 
gesture for the participant’s time. The participant will also be briefed on their right to 
withdraw at any point in the study, the steps taken to ensure confidentiality of the 
information collected as part of the study and the risks (which are minimal) of 
participating. For those who consent, a signature / thumb impression and the date will be 
recorded in the consent form. The verbal explanation of the consent process and the 
forms will be available in both Hindi and Bengali – the local languages spoken in the 

Assessing the effectiveness of Large Language Model 
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14 
 
study sites. A separate signed consent will be obtained for the small group of patients 
selected for the IDIs.  
 
Doctors and nurses will also be asked to consent for their participation in the study. Both 
forms will elaborate on the purpose and objectives of the study, the expectations from the 
individuals, confidentiality issues,  and their right to voluntary withdraw from the study. 
For the nurses, the same consent form will also cover their participation in an in-depth 
interview which will last for 45 to 60 minutes. Signatures will be collected for all 
consenting doctors and nurses. These consent forms will be in English.  
XI. Study Risks 
This study involves minimal risk to participants. The following potential risks have been 
identified, along with corresponding mitigation strategies: 
1. Breach of confidentiality 
Risk: The study involves collection of audio recordings and clinical data, which could 
lead to unintended disclosure of personal health information. 
 Mitigation: All data will be de-identified prior to transcription and analysis. Unique 
participant IDs will be used in place of names or other identifiers. Files will be stored in a 
password-protected, encrypted cloud-based system with access limited to authorized 
members of the research team. 
2. Discomfort due to audio recording 
Risk: Patients, doctors, or nurses may feel uncomfortable being audio recorded during 
clinical consultations or interviews. 
 Mitigation: Participation in audio recording is entirely voluntary. During the informed 
consent process, participants will be clearly informed about the purpose of the recordings, 
how they will be used, and the steps taken to protect their privacy. Participants may 
decline recording or withdraw at any time without penalty. 
3. Disruption to clinical workflow 
Risk: Dual consultations (doctor and nurse + AI) may extend visit time, disrupt routine 
clinic operations, or increase patient burden. 
 Mitigation: Study scheduling will be coordinated to avoid peak hours and minimize 
operational disruption. Participants will be informed of the additional time commitment 
during consent and compensated appropriately for their time. 
4. Emotional or cognitive burden during nurse interviews 

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Risk: Nurses may experience discomfort or fatigue during the in-depth interviews. 
 Mitigation: Participation in interviews is voluntary. Interviews will be conducted in a 
semi-private setting and will be scheduled at convenient times. Nurses may skip 
questions or terminate the interview at any point without consequence. 
5. Over-reliance on AI recommendations 
Risk: There is a possibility that nurses may depend too heavily on AI outputs, reducing 
their use of independent clinical judgment. 
 Mitigation: All participating nurses will be trained to use the AI tool strictly as a 
decision-support system. The AI model will be vetted for safety and usability in advance. 
Clinical supervision will be available during the implementation phase. It will also be 
reiterated to the patients that recommendations from nurse are for study purposes only - 
and that they should only follow the doctor’s clinical advice.  
6. Sequence effects and response bias 
Risk: The order in which patients receive consultations (doctor first vs. nurse + LLM 
first) may influence their experience or survey responses. 
 Mitigation: Participants will be randomly assigned to one of two sequences. The exit 
survey will be administered immediately after the nurse + LLM consultation and will 
focus only on that experience. The analysis will control for consultation order to adjust 
for sequence effects. Survey language will be neutrally framed to avoid bias. 
XI. 
Timeline 
Below is an abridged version of the timeline. See attached MS excel sheet for a detailed 
workplan by site.  
Activities 
Aug 
2025 
Sept 
Oct 
Nov 
Dec 
Jan 
2026 
Feb 
Mar 
Apr 
Ma
y 
Protocol 
finalization 
& IRB 
submission 
X 
 
 
 
 
 
 
 
 
 
Study team 
recruitment 
X 
X 
 
 
 
 
 
 
 
 
Curriculum 
development 
X 
 
 
 
 
 
 
 
 
 

Assessing the effectiveness of Large Language Model 
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Training 
 
X 
X 
 
 
 
 
 
 
 
HCD 
workshop 
 
 
 
X 
 
 
 
 
 
 
Pilot testing 
& primary 
data 
collection 
 
 
 
 
X 
X 
X 
X 
 
 
Analysis, 
report 
writing 
 
 
 
 
 
 
 
 
X 
X 
 
XII. 
Dissemination Plan 
Findings from this study will be shared with key stakeholders to inform the responsible 
use of AI-assisted, nurse-led care in primary health settings. Local dissemination will 
include feedback sessions with participating nurses, clinic staff, and district health 
officials, where results will be presented in non-technical language and local translations 
where needed. Summary findings will also be shared with the State Health Department to 
support future planning and scale-up discussions. Broader dissemination will include 
presentations at academic and public health forums, and publication in peer-reviewed 
journals. All results will be reported in aggregate, with no individual- or facility-level 
identifiers. Participants will not be named in any dissemination materials. Any materials 
used for training or implementation improvements will be shared openly and credited 
appropriately. The dissemination process will prioritize respectful engagement with 
participants and health system partners and will uphold principles of confidentiality, 
transparency, and equitable knowledge sharing. 
XIII. Potential Benefits 
While there is no direct clinical benefit guaranteed to participants, this study has the 
potential to generate significant benefits at the individual, health system, and policy 
levels. For patients, the study may enhance the quality of care received by enabling more 
structured and thorough consultations, particularly with AI-supported clinical reasoning. 
For nurses, the intervention offers an opportunity to build confidence in clinical decision-
making with supportive technology, potentially improving job satisfaction and 
professional autonomy. At the health system level, the findings may inform scalable 
strategies for addressing provider shortages in rural and resource-limited settings by 

Assessing the effectiveness of Large Language Model 
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empowering frontline workers with AI-enabled tools. More broadly, this study will 
contribute critical real-world evidence on the safety, usability, and acceptability of large 
language models in primary care delivery in LMIC settings—an area with limited 
existing research. These insights can guide future investments and policy decisions on 
responsible AI integration in healthcare. 
XIV. Limitations 
This study has several limitations.  
i. 
First, as a clinic-based implementation study conducted in selected rural primary 
care settings, findings may not be generalizable to other regions or higher-level 
health facilities.  
ii. 
Second, the evaluation of clinical quality is based on physician-adjudicated 
scoring of recorded interactions, which, despite using standardized criteria, may 
introduce some subjectivity.  
iii. 
Third, while patient satisfaction is appropriately measured through self-report, 
there remains potential for social desirability bias, even with the use of tablet-
based, self-administered surveys. Although this approach reduces interviewer 
influence and increases privacy, patients may still moderate their responses due to 
perceived surveillance or limited digital literacy.  
iv. 
Fourth, the in-depth interviews with nurses are qualitative and based on a small 
sample, which may not capture the full range of experiences and could be 
influenced by recall bias.  
v. 
Lastly, the study captures short-term implementation outcomes and does not 
assess long-term effectiveness, sustainability, or cost-effectiveness, which will 
require further research. 
References 
Abductive Coding in Qualitative Research: A Practical Guide. (2025, March 31). Delve. 
https://delvetool.com/blog/abductive-coding 
 
Ambikapur Metropolitan Urban Region Population 2011-2025 Census. (n.d.). Retrieved 
August 12, 2025, from https://www.census2011.co.in/census/metropolitan/240-
ambikapur.html 
 
Anjana, R. M., Unnikrishnan, R., Deepa, M., Pradeepa, R., Tandon, N., Das, A. K., Joshi, 
S., Bajaj, S., Jabbar, P. K., Das, H. K., Kumar, A., Dhandhania, V. K., Bhansali, A., Rao, 
P. V., Desai, A., Kalra, S., Gupta, A., Lakshmy, R., Madhu, S. V., … Ghosh, S. (2023). 
Metabolic non-communicable disease health report of India: The ICMR-INDIAB 

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national cross-sectional study (ICMR-INDIAB-17). The Lancet Diabetes & 
Endocrinology, 11(7), 474–489. https://doi.org/10.1016/S2213-8587(23)00119-5 
 
Arora, R. K., Wei, J., Hicks, R. S., Bowman, P., Quiñonero-Candela, J., Tsimpourlas, F., 
Sharman, M., Shah, M., Vallone, A., Beutel, A., Heidecke, J., & Singhal, K. (2025). 
HealthBench: Evaluating Large Language Models Towards Improved Human Health 
(arXiv:2505.08775). arXiv. https://doi.org/10.48550/arXiv.2505.08775 
 
Darby, C., Valentine, N., Murray, C. J., & de Silva, A. (2000). World Health 
Organization (WHO): Strategy on Measuring Responsiveness. 
 
Desk, E. (2025, January 31). Economic survey flags gaps in India’s medical education 
system, calls for reforms. EdexLive. 
https://www.edexlive.com/news/2025/Jan/31/economic-survey-flags-gaps-in-indias-
medical-education-system-calls-for-reforms 
 
Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., 
Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P. J., Rodman, 
A., & Chen, J. H. (2024). Large Language Model Influence on Diagnostic Reasoning: A 
Randomized Clinical Trial. JAMA Network Open, 7(10), e2440969. 
https://doi.org/10.1001/jamanetworkopen.2024.40969 
 
Marshall, G. N., & Hays, R. D. (1994). The Patient Satisfaction Questionnaire Short 
Form (PSQ-18). https://www.rand.org/pubs/papers/P7865.html 
 
Nagari Village Population—Suri—I - Birbhum, West Bengal. (n.d.). Retrieved August 12, 
2025, from https://www.census2011.co.in/data/village/317069-nagari-west-bengal.html 
 
Nong, P., & Platt, J. (2025). Patients’ Trust in Health Systems to Use Artificial 
Intelligence. JAMA Network Open, 8(2), e2460628. 
https://doi.org/10.1001/jamanetworkopen.2024.60628 
 
Nori, H., Daswani, M., Kelly, C., Lundberg, S., Ribeiro, M. T., Wilson, M., Liu, X., 
Sounderajah, V., Carlson, J., Lungren, M. P., Gross, B., Hames, P., Suleyman, M., King, 
D., & Horvitz, E. (2025). Sequential Diagnosis with Language Models 
(arXiv:2506.22405). arXiv. https://doi.org/10.48550/arXiv.2506.22405 
 
Rao, A., Pang, M., Kim, J., Kamineni, M., Lie, W., Prasad, A. K., Landman, A., Dreyer, 
K., & Succi, M. D. (2023). Assessing the Utility of ChatGPT Throughout the Entire 
Clinical Workflow: Development and Usability Study. Journal of Medical Internet 
Research, 25(1), e48659. https://doi.org/10.2196/48659 
 

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Salvi, S., Apte, K., Madas, S., Barne, M., Chhowala, S., Sethi, T., Aggarwal, K., 
Agrawal, A., & Gogtay, J. (2015). Symptoms and medical conditions in 204 912 patients 
visiting primary health-care practitioners in India: A 1-day point prevalence study (the 
POSEIDON study). The Lancet Global Health, 3(12), e776–e784. 
https://doi.org/10.1016/S2214-109X(15)00152-7 
 
Tu, T., Schaekermann, M., Palepu, A., Saab, K., Freyberg, J., Tanno, R., Wang, A., Li, 
B., Amin, M., Cheng, Y., Vedadi, E., Tomasev, N., Azizi, S., Singhal, K., Hou, L., 
Webson, A., Kulkarni, K., Mahdavi, S. S., Semturs, C., … Natarajan, V. (2025). Towards 
conversational diagnostic artificial intelligence. Nature, 642(8067), 442–450. 
https://doi.org/10.1038/s41586-025-08866-7 
 
Update on Ratio Of Patients And Doctors Nurses. (2023, December 12). 
https://www.pib.gov.in/www.pib.gov.in/Pressreleaseshare.aspx?PRID=1985423 
 
 

Assessing the effectiveness of Large Language Model 
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Annexure 1 – Grading rubric 
Patient Overview:  
1. Age  
2. Patient unique study ID -  
3. Nurse ID - 
4. Doctor ID -  
5. Time spent in nurse encounter - 
6. Time spent in doctor encounter -  
7. Study Complaint - (If has more than 1, select multiple) 
a. Diabetes  
b. Hypertension 
c. Breathlessness 
d. Fever 
e. Musculoskeletal Pain 
8. Any system challenges due to internet or electricity (Yes/No) 
 
Grader Sheet for symptoms (Fever, Breathlessness, Musculoskeletal pain) 
Evaluation of nurse + LLM – patient encounter 
S/N Criteria 
S/N Criteria 
1. 
Differential Dx 1 
a. 0 
b. 1 
 
6. 
Next Step 2 
a. -1 
b. 0 
c. 1 
 
2. 
Differential Dx 2 
a. 0 
b. 1 
 
7. 
Next step 3 
a. -1 
b. 0 
c. 1 
 
3. 
Differential Dx 3 
a. 0 
b. 1 
 
8. 
Next step 4 
a. -1 
b. 0 
c. 1 
 
4. 
Final Diagnosis 
a. 0 
b. 1 
c. 2 
 
9. 
Next step 5 
a. -1 
b. 0 
c. 1 
 
5. 
Next step 1 
a. -1 
b. 0 
c. 1 
 
 

Assessing the effectiveness of Large Language Model 
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Evaluation of doctor – patient encounter 
S/N Criteria 
S/N 
Criteria 
1. 
Differential Dx 1 
c. 0 
d. 1 
 
6. 
Next Step 2 
d. -1 
e. 0 
f. 1 
 
2. 
Differential Dx 2 
c. 0 
d. 1 
 
7. 
Next step 3 
d. -1 
e. 0 
f. 1 
 
3. 
Differential Dx 3 
c. 0 
d. 1 
 
8. 
Next step 4 
d. -1 
e. 0 
f. 1 
 
4. 
Final Diagnosis 
d. 0 
e. 1 
f. 2 
 
9. 
Next step 5 
d. -1 
e. 0 
f. 1 
 
5. 
Next step 1 
d. -1 
e. 0 
f. 1 
 
 
 
Grader Sheet for Disease  (Diabetes and Hypertension) 
Evaluation of nurse + LLM – patient encounter 
S/N 
Criteria 
S/N 
Criteria 
1. 
For diabetes and hypertension - Review 
of data 
a. 0 
b. 1 
c. 2 
4. 
Counselling provided 
a. -1 
b. 0 
c. 1 
 
2. 
Screened for complications of the 
disease 
d. 0 
e. 1 
f. 2 
5. 
Treatment plan appropriate 
a. -1 
b. 0 
c. 1 
 
3. 
Medication compliance assessed 
g. 0 
 
 

Assessing the effectiveness of Large Language Model 
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h. 1 
 
Evaluation of doctor – patient encounter 
S/N 
Criteria 
S/N 
Criteria 
1. 
For diabetes and hypertension - Review 
of data 
a. 0 
b. 1 
c. 2 
4. 
Counselling provided 
a. -1 
b. 0 
c. 1 
 
2. 
Screened for complications of the 
disease 
a. 0 
b. 1 
c. 2 
5. 
Treatment plan appropriate 
a. -1 
b. 0 
c. 1 
 
3. 
Medication compliance assessed 
a. 0 
b. 1 
 
 
 
 
 

Assessing the effectiveness of Large Language Model 
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Annexure – 2 – Patient exit survey 
This survey is designed to assess your experience with today’s visit. Your responses will 
help us improve care. 
Section A: Communication and Understanding 
1. The nurse understood my health problem clearly and followed up with questions 
adequately.  
☐ Strongly agree  ☐ Agree  ☐ Neutral  ☐ Disagree  ☐ Strongly disagree 
2. I understood the advice or next steps given to me.  
☐ Strongly agree  ☐ Agree  ☐ Neutral  ☐ Disagree  ☐ Strongly disagree 
3. I had enough time to ask questions. 
☐ Strongly agree  ☐ Agree  ☐ Neutral  ☐ Disagree  ☐ Strongly disagree 
Section B: Trust and Comfort with AI Use 
4. I was comfortable that the nurse used a computer or phone to assist my care. (Nong & 
Platt) 
☐ Strongly agree  ☐ Agree  ☐ Neutral  ☐ Disagree  ☐ Strongly disagree 
5. I trust the recommendations given during the visit.  
☐ Strongly agree  ☐ Agree  ☐ Neutral  ☐ Disagree  ☐ Strongly disagree 
Section C: Respect and Satisfaction 
6. I felt respected during the visit. 
☐ Strongly agree  ☐ Agree  ☐ Neutral  ☐ Disagree  ☐ Strongly disagree 
7. I am satisfied with the care I received today.  
☐ Very satisfied  ☐ Satisfied  ☐ Neutral  ☐ Unsatisfied  ☐ Very unsatisfied 
8. I would be willing to receive care like this again. 
☐ Yes  ☐ Maybe  ☐ No 
Section D: Open Feedback (Optional) 
9. What did you like most about the visit? 
__________________________________________________________________ 
__________________________________________________________________ 
10. How could we improve your experience? 
__________________________________________________________________ 
__________________________________________________________________ 
 
 
 

Assessing the effectiveness of Large Language Model 
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Annexure – 2.1 – Patient exit survey (Bengali) 
!রাগী মতামত ফম) – নাস) + কি.উটার সহায়তায় িচিকৎসা 
সময় লাগেব: ৫ িমিনেটর কম 
এই ফম23 আজেকর আপনার িচিকৎসা অিভ;তা স=েক2  জানেত চায়। আপনার মতামত 
আমােদর িচিকৎসা প?িত আরও ভােলা করেত সাহাযC করেব। 
অংশ A: কথা বলা ও *বাঝার অিভ/তা 
1. ১। নাস% আমার সমস)াটা ভােলাভােব ব/েঝেছন এবং 4েয়াজনীয় 48 কেরেছন। 
☐ পুেরাপুির একমত  ☐ একমত  ☐ না একমত, না অমত  ☐ অমত  ☐ পুেরাপুির অমত 
2. ২। আমােক যা বলা হেয়েছ বা =যভােব পরবতী@  ধাপ =বাঝােনা হেয়েছ, আিম তা ব/ঝেত =পেরিছ। 
☐ পুেরাপুির একমত  ☐ একমত  ☐ না একমত, না অমত  ☐ অমত  ☐ পুেরাপুির অমত 
3. ৩। আমার িনেজর 48Dেলা িজEাসা করার মেতা সময় আিম =পেয়িছ। 
☐ পুেরাপুির একমত  ☐ একমত  ☐ না একমত, না অমত  ☐ অমত  ☐ পুেরাপুির অমত 
অংশ B: কি1উটার ব4বহাের আ8া ও 9ি: 
4. ৪। নাস% আমার িচিকৎসায় =মাবাইল বা কিJউটার ব)বহার কেরেছন – এেত আিম LিM =বাধ কেরিছ। 
☐ পুেরাপুির একমত  ☐ একমত  ☐ না একমত, না অমত  ☐ অমত  ☐ পুেরাপুির অমত 
5. ৫। আজেকর িচিকৎসায় =য পরামশ% =দওয়া হেয়েছ, আিম তার ওপর আRা রাখেত পারিছ। 
☐ পুেরাপুির একমত  ☐ একমত  ☐ না একমত, না অমত  ☐ অমত  ☐ পুেরাপুির অমত 
অংশ C: স<ান ও স>? 
6. ৬। িচিকৎসার সময় আিম সUািনত অনুভব কেরিছ। 
☐ পুেরাপুির একমত  ☐ একমত  ☐ না একমত, না অমত  ☐ অমত  ☐ পুেরাপুির অমত 
7. ৭। আজেকর িচিকৎসায় আিম সYZ। 
☐ খFবই সGH  ☐ সGH  ☐ Iমাটামু3  ☐ অসGH  ☐ এেকবাের অসGH 
8. ৮। আিম ভিবষ)েত এমন িচিকৎসা িনেত আ]হী। 
☐ হঁCা  ☐ হয়েতা  ☐ না 

Assessing the effectiveness of Large Language Model 
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অংশ D: আপনার মতামত (ঐিCক) 
৯। আপনার আজেকর Iদখায় সবেচেয় ভােলা লাগার িবষয় কী িছল? 
__________________________________________________________________ 
__________________________________________________________________ 
১০। আমরা কীভােব আপনার অিভ;তা আরও ভােলা করেত পাির? 
__________________________________________________________________ 
__________________________________________________________________ 
 
 

Assessing the effectiveness of Large Language Model 
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Annexure – 2.2 – Patient exit survey (Hindi) 
रोगी सव' फॉम+ – नस+ + कं/यूटर सहायता से क7 गई देखभाल 
समय: लगभग 5 िमनट लगेंगे 
इस सवेर् का उद्देश्य आज की आपकी िविज़ट का अनुभव जानना है। आपके जवाब हमें सेवा सुधारने में 
मदद करेंगे। 
से#शन A: बातचीत और समझ 
1. नसर् ने मेरी बीमारी को अच्छे से समझा और मुझसे ज़रूरी सवाल पूछे। 
• 
☐ पूरी तरह सहमत हूँ  ☐ सहमत हूँ  ☐ िनिश्चंत नहीं हूँ  ☐ असहमत हूँ  ☐ िबल्कुल असहमत हूँ 
2. मुझे जो सलाह या आगे क्या करना है, वो अच्छी तरह समझ में आया। 
• 
☐ पूरी तरह सहमत हूँ  ☐ सहमत हूँ  ☐ िनिश्चंत नहीं हूँ  ☐ असहमत हूँ  ☐ िबल्कुल असहमत हूँ 
3. मुझे अपने सवाल पूछने के िलए पयार्प्त समय िमला। 
• 
☐ पूरी तरह सहमत हूँ  ☐ सहमत हूँ  ☐ िनिश्चंत नहीं हूँ  ☐ असहमत हूँ  ☐ िबल्कुल असहमत हूँ 
से#शन B: कं1यूटर / मोबाइल से इलाज म9 भरोसा और सहजता 
4. नसर् ने मेरे इलाज में कंप्यूटर या मोबाइल का इस्तेमाल िकया, इससे मुझे कोई परेशानी नहीं हुई। 
• 
☐ पूरी तरह सहमत हूँ  ☐ सहमत हूँ  ☐ िनिश्चंत नहीं हूँ  ☐ असहमत हूँ  ☐ िबल्कुल असहमत हूँ 
5. मुझे जो सलाह दी गई, उस पर मुझे भरोसा है। 
• 
☐ पूरी तरह सहमत हूँ  ☐ सहमत हूँ  ☐ िनिश्चंत नहीं हूँ  ☐ असहमत हूँ  ☐ िबल्कुल असहमत हूँ 
से#शन C: स<मान और संतुि?ट 
6. इस िविज़ट के दौरान मुझे पूरा सम्मान िमला। 
• 
☐ पूरी तरह सहमत हूँ  ☐ सहमत हूँ  ☐ िनिश्चंत नहीं हूँ  ☐ असहमत हूँ  ☐ िबल्कुल असहमत हूँ 
7. आज मुझे जो देखभाल िमली, उससे मैं िकतना संतुष्ट हूँ: 
• 
☐ बहुत संतुष्ट  ☐ संतुष्ट  ☐ ठीक-ठाक  ☐ असंतुष्ट  ☐ िबल्कुल असंतुष्ट 
8. क्या आप भिवष्य में भी इसी तरह की देखभाल लेना चाहेंगे? 
• 
☐ हाँ  ☐ शायद  ☐ नहीं 

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से#शन D: खुलA BCतDEया (वैकिHपक) 
9. आज की िविज़ट में आपको सबसे अच्छा क्या लगा? 
__________________________________________________________________ 
__________________________________________________________________ 
10. हम आपकी सेवा को और बेहतर कैसे बना सकते हैं? 
__________________________________________________________________ 
__________________________________________________________________ 
 
 

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Annexure 3 – In-depth interview guide for patients 
In-Depth Interview Guide: Patient Experience with Nurse + AI Consultation 
Estimated Duration: 30–45 minutes 
Purpose: To explore in greater depth how patients perceived the nurse-led 
consultation assisted by an AI tool. This includes their understanding, trust, comfort, 
and overall acceptability of this new model of care. 
Section 1: Introduction  
Thank you for agreeing to speak with us today. This interview is about your 
experience during your recent visit to the clinic. Your responses will help us 
improve how nurses and technology work together to provide better care. 
Everything you say will remain confidential, and your name will not be used in any 
reports. You can choose to skip any question or stop the interview at any time. 
Do you have any questions before we start? 
Section 2: Communication and Understanding  
1. What was the reason for your clinic visit today?  
2. How well did the nurse seem to understand your problem? 
a. Did the nurse ask you enough questions? 
3. How was the counseling you received from the nurse? 
a. Were the next steps or advice adequately explained to you? 
b. Did the nurse answer your questions in a way that you could understand? 
 
4. Did you feel like you had enough time to ask your questions? 
a. Were any medical words or terms used that were hard to follow? 
Section 3: Comfort and Trust with AI Tool  
5. During your consultation, you may have noticed the nurse using a mobile phone or 
computer. How did you feel about that? Please explain 
a. Did the nurse tell you why they were using the device? 
 
6. Did the use of the phone/computer make you more confident or more unsure of the 
clinical counseling you received from the nurse? 
a. Did you feel that the care or advice was different because a computer tool 
was being used? 
7. In your opinion, did you feel the tool helped or got in the way of the clinical 
consultation? 
a. Do you think the nurse’s time spent in talking with you was affected because 
of presence of a phone or computer?  
b. If it was affected, was it a positive or negative effect? Please elaborate.  

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Section 4: Respect, Satisfaction, and Acceptability  
8. How did you feel you were treated during the visit? 
a. Did you feel listened to and respected? 
b. Overall, how satisfied were you with the care you received? 
9. Would you feel comfortable coming back for a clinical visit like this (with a nurse + 
AI tool) again? 
10. Would you recommend this kind of care to a friend or family member? 
Section 5: Open Feedback and Suggestions 
11. What did you like most about your visit today? 
12. Was there anything you didn’t like or found confusing? 
13. How could we improve this type of care in the future? 
a. If we used this tool in more clinics, what would you want us to keep the 
same or change? 
Section 6: Closing  
Thank you very much for your time and feedback. We appreciate your help in 
making healthcare better for everyone. 
 
 

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Annexure – 3.1 – In-depth interview guide for patients (Bengali) 
গভীর সাQাৎকার িনেদ2 িশকা: নাস2 + AI সহ Iরাগীর অিভ;তা 
সময়কাল: আনুমািনক ৩০–৪৫ িমিনট 
উেVশC: নাস2 এবং AI টF েলর সাহােযC হওয়া িচিকৎসা পরামশ2 Iকমন িছল, Iরাগীরা কীভােব তা 
বFেঝেছ, িবYাস কেরেছ, Zি[ অনুভব কেরেছ – এই সব জানার জনC। 
ধারাবািহক অংশ: 
১. পিরিচিত  
আজ আমােদর সে] কথা বলার জনC ধনCবাদ। এই সাQাৎকার আপনার ি_িনেক আসার 
অিভ;তা িনেয়। আপনার মতামত আমােদর সাহাযC করেব নাস2 এবং `যFিaেক একসােথ বCবহার 
কের আরও ভােলা পিরেষবা িদেত। আপনার সম[ উbর Iগাপন রাখা হেব। আপিন Iযেকােনা `c 
এিড়েয় Iযেত পােরন বা Iযেকােনা সময় সাQাৎকার বe করেত পােরন। 
fরg করার আেগ Iকােনা `c আেছ িক? 
২. Iযাগােযাগ এবং Iবাঝাপড়া  
- আপিন আজ ি_িনেক Iকন এেসিছেলন? 
- নাস2 িক আপনার সমসCা বFেঝিছেলন বেল মেন হেয়েছ? 
- নাস2 িক আপনােক পয2াi `c কেরিছেলন? 
- নাস2 Iয পরামশ2 িদেয়েছন তা িক আপিন বFঝেত Iপেরেছন? 
- আপিন িক িনেজর `c করেত সময় Iপেয়েছন? 
- Iকােনা Iমিডেকল শk িক বFঝেত অসুিবধা হেয়েছ? 
৩. AI টF ল িনেয় Zি[ ও িবYাস  
- নাস2 যিদ Iমাবাইল বা কি=উটার বCবহার কেরন, তাহেল আপিন Iকমন অনুভব কেরিছেলন? 
- নাস2 িক আপনােক জািনেয়েছন Iকন িডভাইস বCবহার করেছন? 
- AI বCবহাের আপনার িবYাস Iবেড়েছ না কেমেছ? 
- এই টF ল বCবহাের পরামশ2 বা পিরেষবায় Iকােনা পাথ2কC মেন হেয়েছ িক? 
- টF ল3 িক সাহাযC কেরেছ, না িক বCাঘাত ঘ3েয়েছ বেল মেন হেয়েছ? 
- নাস2 িক যেথH সময় িদেত Iপেরেছন, না িক Iফান/কি=উটার বCবহাের সময় কম Iপেয়েছন? 
- যিদ কম Iপেয় থােকন, তাহেল Iসটা ভােলা না খারাপ হেয়েছ বেল মেন হেয়েছ? 
 
৪. সmান, সGn এবং oহণেযাগCতা  
- এই িভিজেট আপিন Iকমন অনুভব কেরেছন? 
- আপিন িক মেন কেরন আপনােক সmান Iদওয়া হেয়েছ? 
- আপিন িক িচিকৎসায় সGH? 
- ভিবষCেত আবার এইরকম নাস2 + AI Iসবা িনেত চাইেবন? 

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- আপিন িক এই পিরেষবা3 বeF বাeব বা পিরবােরর সদসCেদর সুপািরশ করেবন? 
 
৫. Iখালা মতামত ও পরামশ2  
- আজেকর িভিজেট আপনার সবেচেয় ভােলা Iলেগেছ Iকানটা? 
- এমন িকছF  িক িছল যা আপিন Iবােঝনিন বা পছq কেরনিন? 
- ভিবষCেত এই পিরেষবা কীভােব উrত করা যায়? 
- যিদ এই টF ল আরও ি_িনেক বCবsত হয়, তাহেল Iকান িজিনস অপিরবtতuত রাখা উিচত আর কী 
পিরবত2ন দরকার? 
৬. সমািi  
আপনার সময় ও আwিরক মতামেতর জনC অেনক ধনCবাদ। এ3 সকেলর জনC ZাxCেসবা আরও 
ভােলা করেত সাহাযC করেব। 
 

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Annexure – 3.2 – In-depth interview guide for patients (Hindi) 
गहन साक्षात्कार मागर्दिशर्का: नसर् + AI परामशर् के साथ मरीज का अनुभव 
अनुमािनत समय: 30–45 िमनट 
उद्देश्य: यह समझना िक मरीजों ने नसर् और AI टूल की मदद से की गई परामशर् को कैसे अनुभव िकया – 
उनकी समझ, िवश्वास, आराम और इस देखभाल मॉडल की स्वीकृित के बारे में। 
अनुभाग 1: पिरचय  
धन्यवाद, आपने हमसे बात करने के िलए समय िनकाला। यह बातचीत आपके हाल के िक्लिनक िविज़ट 
के अनुभव के बारे में है। आपकी बातें हमें नसर् और तकनीक के साथ िमलकर बेहतर सेवा देने में मदद 
करेंगी। आपकी पहचान गोपनीय रखी जाएगी और आपका नाम िकसी िरपोटर् में नहीं आएगा। आप िकसी 
भी सवाल का जवाब देने से मना कर सकते हैं या जब चाहें बातचीत रोक सकते हैं। 
क्या शुरू करने से पहले आपके कोई सवाल हैं? 
अनुभाग 2: संवाद और समझ  
- आप आज िकस वजह से िक्लिनक आए थे? 
- क्या नसर् ने आपकी समस्या को अच्छे से समझा? 
- क्या नसर् ने आपसे पयार्प्त सवाल पूछे? 
- नसर् ने आपको जो समझाया, क्या वह ठीक से समझ में आया? 
- क्या आपकी बातों को ध्यान से सुना गया? 
- क्या आपके पास अपने सवाल पूछने के िलए समय था? 
- क्या िकसी मेिडकल शब्द को समझने में किठनाई हुई? 
 
अनुभाग 3: AI टूल के साथ आराम और िवश्वास  
 
- परामशर् के दौरान आपने नसर् को मोबाइल या कंप्यूटर इस्तेमाल करते देखा होगा। इससे आपको कैसा 
महसूस हुआ? 
- क्या नसर् ने बताया िक वह मोबाइल/कंप्यूटर क्यों इस्तेमाल कर रही हैं? 
- क्या इस तकनीक के उपयोग से आपकी नसर् की सलाह पर िवश्वास बढ़ा या घटा? 
- क्या आपको लगा िक AI टूल से सलाह या इलाज में कुछ बदलाव आया? 
- क्या आपको लगा िक यह टूल मददगार था या उलझन पैदा कर रहा था? 
- क्या नसर् आपके साथ पयार्प्त समय िबता पाईं, या फोन/कंप्यूटर की वजह से समय कम िमला? 
- अगर फकर् पड़ा, तो वह अच्छा था या बुरा? कृपया समझाएं। 
 
अनुभाग 4: सम्मान, संतुिष्ट और स्वीकायर्ता  
- आपको इस परामशर् के दौरान कैसा महसूस हुआ? 

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- क्या आप महसूस करते हैं िक आपकी बात सुनी गई और सम्मान िमला? 
- क्या आप आज की सेवा से संतुष्ट हैं? 
- क्या आप दोबारा इस तरह की नसर् + AI सेवा लेना चाहेंगे? 
- क्या आप अपने पिरवार या दोस्तों को यह सेवा लेने की सलाह देंगे? 
 
अनुभाग 5: सुझाव और खुला फीडबैक  
- आज की िविज़ट में आपको सबसे अच्छा क्या लगा? 
- क्या कोई बात थी जो आपको समझ नहीं आई या पसंद नहीं आई? 
- भिवष्य में इस सेवा को बेहतर कैसे बनाया जा सकता है? 
- अगर यह टूल और िक्लिनक में इस्तेमाल हो, तो आप क्या चीज़ वैसी ही रखना चाहेंगे और क्या 
बदलवाना चाहेंगे? 
अनुभाग 6: समािप्त  
आपका बहुत धन्यवाद िक आपने समय िनकाला और ईमानदारी से अपनी राय दी। यह हमारी सेवा को 
बेहतर बनाने में मदद करेगा। 
 
 

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Annexure – 4 – In-depth interview guide for nurses 
Title: In-depth interview guide for assessing the perceptions & acceptability of AI tool 
use by nurses in primary health care settings in rural India. 
Icebreaker 
1. How long have you been a nurse? Where did you work prior to this project? 
2. How many patients have you seen during this project?  
3. How has the experience been? 
Training 
4. You received training on medicine-specific topics and digital literacy prior to the start of the 
project.  What is your opinion on the training material? 
a. Did you feel enough time was spent on medical literacy? How can it be made 
better? 
b. What is your opinion on the depth of teaching done for digital literacy? What 
changes should be made? 
c. What was your opinion about the training material ? Was it easy to follow? 
Please elaborate. 
5. How was the teaching? Were you able to follow the language used? 
6. How do you feel about the frequency of classes / sessions? Should it be changed? If yes, what 
should the change be? 
7. The trainings were delivered to you online. How was your experience with online learning?  
a. Did the sessions allow you sufficient opportunity to ask questions? 
8. What suggestions do you have to improve the training experience ? 
Feasibility 
9. You used an AI tool to see patients. You spoke to both the patient and the AI tool during the 
consult. How comfortable were you with this process?  
a. Were you able to use the tool with patients without major interruptions? How did 
it alter your workflow? 
10. Would you rate the AI tool as an enabler or hindrance to your workflow?  Please elaborate. 
Acceptability 
11. How did you feel using the AI tool during patient care? 
12. Do you trust the recommendations it gave? 
13. If there was an option to permanently integrate a similar tool in your daily work where you 
could see patients for common conditions and complaints, would you want to continue doing 
it?  
Practical considerations 
14. You communicated with the AI tool through text/ voice/ both. How was your experience 
using these modalities?  

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a. How can it be improved? Single mode? Voice or text only? 
15. Did internet connection affect the smoothness of your workflow? 
a. If yes, how was your work affected? 
Learnings 
16. If you had to give 1 advice to the people who designed the nurse-AI intervention project, 
what would that be? 
 
 

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Annexure – 4.1 – In – depth interview guide for nurses (Bengali) 
গভীর সা6াৎকােরর গাইড 
`কy: oামীণ `াথিমক ZাxC বCবxায় নাস2েদর zারা এআই টF ল বCবহােরর অিভ;তা, oহণেযাগCতা 
িনেয় মূলCায়ন 
তািরখ: ১৫ জুলাই ২০২৫ | সং|রণ: ১ 
Icebreaker 
1) আপিন কতিদন ধের নাস% িহেসেব কাজ করেছন? এই 4কে^ =যাগদােনর আেগ =কাথায় কাজ কেরেছন? 
2) এই 4কে^ আপিন কতজন =রাগী =দেখেছন? 
3) আপনার অিভEতা =কমন হেয়েছ? 
Training 
4) এই 4ক^ aরbর আেগ আপনােক ওষ/ধ সংcাd এবং িডিজটাল িলটােরিস িনেয় training =দওয়া 
হেয়িছল। এই training িবষয়বf িনেয় আপনার কী মতামত? 
a) আপিন িক মেন কেরন =মিডেকল িবষেয় যেথZ সময় =দওয়া হেয়িছল? আরও ভােলা করেত কী করা 
=যেত পাের? 
b) িডিজটাল িলটােরিস =শখােনার গভীরতা িনেয় আপনার কী মতামত? =কােনা পিরবত%েনর দরকার 
আেছ িক? 
c) Training উপকরণ =কমন িছল? =বাঝা িক সহজ িছল? িবMািরত বলুন। 
5) =শখােনার ধরণ =কমন িছল? ভাষা ব/ঝেত পারেতন =তা? 
6) hাস/=সশেনর সংখ)া িনেয় আপনার কী অিভমত? পিরবত%েনর দরকার আেছ িক? থাকেল কী পিরবত%ন 
হওয়া উিচত? 
7) Training Dেলা অনলাইেন হেয়েছ। অনলাইন =শখার অিভEতা =কমন িছল? 
a) আপিন িক 4িতi =সশেন 48 করার যেথZ সুেযাগ =পেয়েছন? 
8) এই training আরও উjত করেত আপনার কী পরামশ% আেছ? 
Feasibility 
9) আপিন =রাগী =দখার সময় একi এআই ট/ ল ব)বহার কেরেছন এবং একই সমেয় =রাগী ও ট/ েলর সেk কথা 
বেলেছন। এই 4িcয়ায় আপনার =কমন =লেগেছ? 
a) আপিন িক =রাগীেদর সেk কাজ করার সময় ট/ লi িনlবmেn ব)বহার করেত =পেরেছন? এi িক 
আপনার কােজর ধরণ পিরবত%ন কেরেছ? 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
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37 
 
10) আপিন িক মেন কেরন এই এআই ট/ লi আপনার কাজ সহজ কেরেছ না িক সমস)ার কারণ হেয়েছ? 
িবMািরত বলুন। 
Acceptability 
11) =রাগীর যেo এই এআই ট/ ল ব)বহার কের আপনার কী অনুভp িত হেয়েছ? 
12) এi =য সুপািরশDেলা িদেয়েছ, আপিন িক =সDেলার ওপর িবqাস =রেখেছন? 
13) যিদ এই ধরেনর ট/ ল আপনার 4িতিদেনর কােজ Rায়ীভােব ব)বহার করার সুেযাগ থােক, আপিন িক তা 
করেত আ]হী হেবন? 
Practical considerations 
14) আপিন এই এআই ট/ েলর সেk =লখা/কr/উভয় মাধ)ম ব)বহার কের =যাগােযাগ কেরেছন। আপনার এই 
অিভEতা =কমন িছল? 
a) কীভােব এi আরও ভােলা করা =যেত পাের? aধ/ একi মাধ)ম রাখা িক ভােলা হেব? কr বা =লখা—
=কানi আপনার জন) সহজ? 
15) ইsারেনট সংেযাগ িক আপনার কােজর গিত বা ধারায় সমস)া tতির কেরেছ? 
a)  যিদ কের থােক, কীভােব আপনার কােজর উপর এর 4ভাব পেড়েছ? 
Learnings 
16) যিদ আপিন এই নাস%-এআই 4ক^ িডজাইনকারী দলেক একi পরামশ% িদেত পােরন, আপিন কী 
বলেবন? 
 
 

Assessing the effectiveness of Large Language Model 
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38 
 
Annexure – 4.2 – In – depth interview guide for nurses (Hindi) 
गहराई से सा=ा>कार गाइड 
उद्देश्य: यह गाइड यह समझने के िलए है िक ग्रामीण भारत में प्राथिमक स्वास्थ्य सेवा में काम कर रही नसेर्ं 
AI टूल का उपयोग करने को लेकर क्या सोचती हैं, इसे अपनाने और आगे भी जारी रखने को लेकर उनकी 
क्या राय है। 
िदनांक: 15 जुलाई 2025 
संस्करण: 1 
पJरचयाKमक सवाल (Icebreaker) 
• 
आप िकतने सालों से नसर् हैं? इस प्रोजेक्ट से पहले आपने कहाँ काम िकया था? 
• 
इस प्रोजेक्ट के दौरान आपने िकतने मरीज देखे हैं? 
• 
अब तक का अनुभव कैसा रहा? 
BLशMण से जुड़े सवाल 
• 
आपको इस प्रोजेक्ट से पहले दवाओं से संबंिधत और िडिजटल ट्रेिनंग दी गई थी। आपको उस ट्रेिनंग के बारे में 
क्या लगा? 
• 
क्या आपको लगता है िक मेिडकल िवषयों पर पयार्प्त समय िदया गया? इसे बेहतर कैसे बनाया जा सकता है? 
• 
िडिजटल िवषयों की ट्रेिनंग कैसी थी? क्या कुछ सुधार िकया जा सकता है? 
• 
आपको ट्रेिनंग सामग्री कैसी लगी? क्या समझना आसान था? कृपया िवस्तार से बताएं। 
• 
पढ़ाने का तरीका कैसा था? क्या आप भाषा ठीक से समझ पा रही थीं? 
• 
क्लासेस िकतनी बार होती थीं — क्या उनकी संख्या ठीक थी या बदलाव चािहए? 
• 
यह ट्रेिनंग ऑनलाइन दी गई थी। ऑनलाइन पढ़ाई का अनुभव कैसा रहा? 
• 
क्या आपको सेशन के दौरान सवाल पूछने का पूरा मौका िमला? 
• 
ट्रेिनंग को और अच्छा बनाने के िलए आपके क्या सुझाव हैं? 
उपयोग कR SयवहाJरकता (Feasibility) 
• 
आपने मरीजों को देखने के िलए AI टूल का उपयोग िकया। एक ही समय में आपने मरीज से और AI से बात 
की। यह प्रिक्रया आपके िलए िकतनी सहज थी? 
• 
क्या आप िबना िकसी रुकावट के टूल का इस्तेमाल कर पाईं? इससे आपके काम के तरीके में क्या बदलाव 
आया? 

Assessing the effectiveness of Large Language Model 
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districts: A pilot study 
39 
 
• 
आपको AI टूल आपके काम में मददगार लगा या रुकावट? कृपया िवस्तार से बताएं। 
TवीकायUता (Acceptability) 
• 
मरीजों को देखने के दौरान AI टूल का उपयोग करके आपको कैसा लगा? 
• 
क्या आपको टूल की सलाहों पर भरोसा था? 
• 
अगर कोई ऐसा टूल रोज़मरार् के काम में हमेशा के िलए जोड़ िदया जाए, तो क्या आप इसका इस्तेमाल करना 
चाहेंगी? 
SयवहाJरक पहलू (Practical considerations) 
• 
आपने AI टूल से बातचीत िलखकर / बोलकर / दोनों तरीकों से की। यह अनुभव कैसा था? 
• 
क्या इसे बेहतर िकया जा सकता है? क्या िसफर् एक तरीका (जैसे िसफर् बोलना या िसफर् िलखना) बेहतर रहेगा? 
• 
क्या इंटरनेट की िस्थित ने आपके काम को प्रभािवत िकया? 
• 
अगर हाँ, तो कैसे असर हुआ? 
सीख (Learnings) 
• 
अगर आपको इस प्रोजेक्ट को िडज़ाइन करने वालों को कोई एक सलाह देनी हो, तो वह क्या होगी? 
 
 

Assessing the effectiveness of Large Language Model 
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40 
 
Annexure – 5 – Consent script for patients 
Study Title: Assessing the effectiveness of Large Language Model (LLM) - enabled 
nurse treatment planning in 2 Indian districts: A pilot study 
Site: Ambikapur, Chhattisgarh 
Principal Investigator: [Insert Name, Contact Number] 
Hello, my name is [Name]. We are conducting a study at this clinic. We are testing a new AI 
tool that supports nurses in managing common health problems like high blood pressure, 
diabetes, joint pain, fever, or breathlessness. 
If you agree to participate, here's what will happen: 
- You will be seen by both a nurse (who will use the AI tool) and a doctor during today's 
visit. You are required to travel to two locations to meet with the two providers (doctor and 
nurse), and we will take care of the transportation arrangements. 
- This may take a little extra time compared to a regular visit. 
- At the end, you’ll be asked to complete a short survey about your experience. 
Please note: The nurse may give some advice based on the AI tool, but this is only for the 
study. You will leave the clinic with the doctor’s prescription. Please follow only the doctor’s 
advice for your treatment. 
There are no medical risks in joining the study. Whether or not you take part, your regular 
care will not be affected. Your personal information will be kept private. 
As a thank you, you will receive INR 250 after completing your visit and the survey. Taking 
part is completely up to you. You can say no or stop at any time. 
If you have questions after the study, you may contact the Principal Investigator at [Insert 
Phone Number], or the local Institutional Review Board (IRB) at [Insert IRB Contact 
Information]. 
Do you have any questions? 
If you agree, we will now ask for your written consent. 
☐ Yes, I agree to participate 
☐ No, I do not wish to participate 
Participant Name & signature : ________________________ 
Date: ________________________ 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
41 
 
Annexure – 5.1 – Consent script for patients (Bengali) 
}ািডর নাম: এআই (কি=উটারচািলত সহায়ক টF ল) বCবহার কের নাস2-Iনত~ াধীন Iসবার মূলCায়ন 
xান: 
}ািডর `ধান দািয়শীল বCিa: [নাম, Iফান নর] 
নম|ার, আমার নাম [নাম]। আমরা এখােন এক3 }ািড করিছ। আমরা এক3 নতF ন এআই টF ল 
(কি=উটারচািলত সহায়ক) পরীQা করিছ যা নাস2েদর আপনার মেতা সমসCার যে সাহাযC কের—
Iযমন high pressure, sugar, হঁাটF র বা হাত-পােয়র বCথা, র, বা YাসকH/Yাস ফF েল যাওয়া। 
আপিন যিদ অংশ িনেত রািজ থােকন, তাহেল আপনােক যা করেত হেব: 
- আজ আপনােক একজন নাস2 (িযিন এআই টF ল বCবহার করেবন) এবং একজন িচিকৎসেকর zারা 
Iদখা হেব। আপনােক দুই3 জায়গায় Iযেত হেব – একবার ডাaার এর সােথ Iদখা করেত এবং 
একবার নাস2 এর সােথ Iদখা করেত।  
- এেত সাধারণ িভিজেটর Iচেয় িকছF টা Iবিশ সময় লাগেত পাের। 
- Iশেষ, আমরা আপনােক এক3 Iছাট সমীQা পূরণ করেত বলব যােত আপিন আপনার অিভ;তা 
জানােত পােরন। 
রgপূণ2: নাস2 এআই টF েলর সাহােযC Iয পরামশ2 Iদেবন তা fধFমা এই }ািডর জনC। আপিন 
িচিকৎসেকর I`সিপশন সহ ি_িনক ছাড়েবন এবং আপনার বত2মান অসুxতার জনC fধFমা 
িচিকৎসেকর পরামশ2 অনুসরণ করা উিচত। 
এই }ািডেত অংশoহেণ Iকানও িচিকৎসাগত ঝFঁিক Iনই। আপিন এই }ািডেত অংশ িনন বা না িনন, 
এ3 আপনার যে Iকানও `ভাব Iফলেব না। আপনার তথC Iগাপন রাখা হেব। 
আপনার সমেয়র ক~ ত;তাZরপ, িভিজট ও জিরপ Iশষ করার পের আপনােক INR 250 `দান করা 
হেব। অংশoহণ পুেরাপুির আপনার ইা। আপিন না বলেত পােরন, বা মাঝপেথ Iথেম Iযেত 
পােরন। }ািড Iশষ হওয়ার পের যিদ Iকানও `c থােক, তাহেল আপিন }ািডর `ধান দািয়শীল 
বCিaর সােথ [Iফান নর িলখFন] নের বা xানীয় ইন3উশনাল িরিভউ Iবাড2  (IRB)-র সােথ 
[IRB Iযাগােযাগ িলখFন] Iযাগােযাগ করেত পােরন। 
আপনার িক Iকানও `c আেছ? 
আপিন যিদ রািজ থােকন, আমরা এখন আপনার Iমৗিখক বা িলিখত সmিত Iনব। 
☐ হঁCা, আিম অংশoহেণ সmত 
☐ না, আিম অংশoহণ করেত চাই না 
 
অংশoহণকারীর নাম (ঐিক): ________________________ 
তািরখ: ________________________ 
 
 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
42 
 
Annexure – 5.2 – Consent script for patients (Hindi) 
अध्ययन का नाम: एआई (कंप्यूटर से चलने वाला सहायक उपकरण) की मदद से नसर् द्वारा दी जाने वाली देखभाल 
का मूल्यांकन 
स्थान: 
मुख्य अध्ययन प्रभारी: [नाम, फ़ोन नंबर] 
नमस्ते, मेरा नाम [नाम] है। हम यहाँ एक अध्ययन कर रहे हैं। हम एक नया एआई टूल (कंप्यूटर से चलने वाला 
सहायक उपकरण) आज़मा रहे हैं जो नसोर्ं को आपकी जैसी बीमािरयों की देखभाल में मदद करता है—जैसे उच्च 
रक्तचाप (ब्लड प्रेशर), शुगर/मधुमेह, जोड़ों का ददर्, बुखार, या सांस फूलना/सांस की तकलीफ़। 
अगर आप इसमें भाग लेने के िलए तैयार हों, तो आपको ये करना होगा: 
- आज की िविज़ट में आपको एक नसर् (जो एआई टूल का उपयोग करेगी) और एक डॉक्टर दोनों देखेंगे। आपको दो 
जगहों पर जाना होगा – एक डॉक्टर से िमलने और एक नसर् से िमलने के िलए। आपकी यात्रा की व्यवस्था हम कर 
देंगे। 
- इससे सामान्य िविज़ट की तुलना में थोड़ा अिधक समय लग सकता है। 
- िविज़ट के अंत में, हम आपसे आपके अनुभव पर एक छोटा फॉमर् / सवेर् भरने को कहेंगे। 
महत्वपूणर्: नसर् जो भी सलाह देगी, वह केवल इस अध्ययन (िरसचर्) के िलए होगी। आपको िक्लिनक से डॉक्टर का 
असली पचार् / इलाज की सलाह िमलेगी। कृपया अपने इलाज के िलए िसफर् डॉक्टर की सलाह का ही पालन करें। 
आपके िलए इसमें कोई अितिरक्त िचिकत्सीय (मेिडकल) जोिखम नहीं है। आप भाग लें या न लें—आपको िमलने 
वाली िनयिमत िचिकत्सा सेवा पर कोई असर नहीं पड़ेगा। आपकी िनजी जानकारी गुप्त रखी जाएगी। 
आपके समय के िलए धन्यवाद के रूप में, िविज़ट और सवेर् पूरा करने के बाद आपको INR 250 िदए जाएंगे। भाग 
लेना पूरी तरह आपकी मज़ीर् है। आप चाहें तो मना कर सकते हैं या बीच में कभी भी रुक सकते हैं। अध्ययन के बाद 
यिद आपके कोई सवाल हों, तो आप मुख्य अध्ययन प्रभारी से [फ़ोन नंबर दजर् करें] पर या स्थानीय नैितक सिमित 
(IRB) से [IRB संपकर् जानकारी दजर् करें] पर बात कर सकते हैं। 
क्या आपके कोई सवाल हैं? 
अगर आप सहमत हैं, तो हम अभी आपकी मौिखक (मुँह से कहकर) या िलिखत सहमित लेंगे। 
☐ हाँ, मैं भाग लेने के िलए सहमत हूँ 
☐ नहीं, मैं भाग नहीं लेना चाहता/चाहती 
प्रितभागी (रोगी) का नाम (वैकिल्पक): ________________________ 

Assessing the effectiveness of Large Language Model 
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तारीख: ________________________ 
 
 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
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Annexure – 6 – Consent script for patients (IDI) 
 
Study Title: Evaluating AI-Assisted Nurse-Led Consultations in Primary Care 
 
Introduction 
Namaste / Nomoskar. My name is [Interviewer’s Name], and I’m part of the research 
team working on a study to improve how nurses and technology can work together to 
provide better healthcare. 
 
We are inviting you to participate in a one-time interview about your experience during 
your recent clinic visit, where you received care from a nurse who used a phone or 
computer (AI tool) to assist in the consultation. 
 
Purpose of the Interview 
The goal of this interview is to better understand what patients like you thought and felt 
about this new way of receiving care. Your feedback will help us improve the quality and 
experience of such care for others. 
 
What Will Happen 
- If you agree, we will talk for about 30 to 45 minutes. 
- We will ask you about how well you understood the consultation, how comfortable you 
felt, and if you would be open to receiving similar care in the future. 
- With your permission, the conversation will be audio recorded, so we don’t miss 
anything important you say. 
 
Voluntary Participation 
- Your participation is completely voluntary. 
- You can refuse to answer any question or stop the interview at any time without giving 
a reason. 
- Saying no will not affect your care in any way. 
 
Privacy and Confidentiality 
- Your name or identity will not be shared in any reports. 
- Audio recordings will be stored securely and only accessed by the research team. 
- If we use any direct quote from your interview in a report or publication, we will only 
do so with your permission. 
 
Risks and Benefits 
There are no direct risks or benefits. But your feedback may help improve how care is 
provided in clinics like this one. 
 
Incentive 

Assessing the effectiveness of Large Language Model 
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As a small thank you for your time, we will provide INR 250 after the interview is 
completed. 
 
Do You Have Any Questions? 
[Pause for any questions] 
 
If you are willing to participate, we will ask you to sign or give your thumb impression 
on a consent form. Do you agree to take part in this interview? 
 

Assessing the effectiveness of Large Language Model 
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Annexure – 6.1 – Consent script for patients (IDI) [Bengali] 
 
গভীর সাQাৎকােরর জনC Iরাগীর সmিত ফম2 
 
পিরিচিত 
নম|ার। আিম [ইারিভউ Iনওয়ার বCিaর নাম], এবং আিম এক3 গেবষণা দেলর সদসC, যারা 
নাস2 ও `যFিaর মাধCেম ZাxCেসবার মান উrত করার জনC কাজ করেছ। 
 
আপনার সািতক ি_িনক িভিজেটর অিভ;তা িনেয় আমরা এক3 এককালীন সাQাৎকার িনেত 
চাই। Iসই সময় আপিন এমন একজন নােস2র কােছ িচিকৎসা Iপেয়িছেলন িযিন Iফান বা কি=উটার 
(AI টF ল) বCবহার কেরিছেলন। 
 
এই সাQাৎকােরর উেVশC 
আমরা বFঝেত চাই, আপিন এই ধরেনর Iসবার িবষেয় কী ভােবন এবং Iকমন অনুভব কেরেছন। 
আপনার মতামত ভিবষCেত এই ধরেনর Iসবার মান আরও উrত করেত সাহাযC করেব। 
 
িক হেব 
- আপিন রািজ হেল, আমরা `ায় ৩০ Iথেক ৪৫ িমিনট কথা বলেবা। 
- আমরা জানেত চাইেবা, আপিন পরামশ2 কতটা বFেঝেছন, কতটা ZাqC Iবাধ কেরেছন, এবং 
ভিবষCেত এই ধরেনর Iসবা িনেত ইF ক িকনা। 
- আপনার অনুমিত িনেয় এই কেথাপকথন অিডও Iরকড2  করা হেব Iযন আমরা িকছF  না িমস কির। 
 
IZােসবী অংশoহণ 
- এই সাQাৎকাের অংশoহণ স=ূণ2 IZােসবী। 
- আপিন Iয Iকােনা `c এড়ােত পােরন বা Iযেকােনা সময় Iথেম Iযেত পােরন। 
- না বলেল আপনার িচিকৎসায় Iকােনা `ভাব পড়েব না। 
 
Iগাপনীয়তা 
- আপনার নাম বা পিরচয় Iকােনা িরেপােট2  বCবহার হেব না। 
- Iরকtডuং িনরাপেদ রাখা হেব এবং fধFমা গেবষণা দেলর সদসCরা এ3 Iদখেত পারেব। 
- যিদ আপনার বaবC Iথেক িকছF  উ?~ িত বCবহার করা হয়, তেব তা আপনার অনুমিত িনেয়ই হেব। 
 
ঝFঁিক ও সুিবধা 
এই সাQাৎকাের Iকােনা ঝFঁিক বা সরাসির উপকার Iনই, তেব আপনার মতামত ZাxCেসবা উrত 
করেত সাহাযC করেত পাের। 

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উপহার 
আপনার সময় Iদওয়ার জনC আমরা Iছাট এক3 উপহার Iদেবা [INR 250]। 
 
আপনার Iকােনা `c আেছ? 
[`েcর জনC িবরিত] 
 
আপিন যিদ রািজ থােকন, তাহেল আমরা এক3 সmিত ফেম2 আপনার ZাQর বা আঙF েলর ছাপ 
Iনেবা। আপিন িক অংশ িনেত সmত? 
 
 

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Annexure – 6.2 – Consent script for patients (IDI) [Hindi] 
गहन साक्षात्कार के िलए मरीज की सहमित िस्क्रप्ट 
 
पिरचय 
नमस्ते। मेरा नाम [साक्षात्कारकतार् का नाम] है और मैं एक िरसचर् टीम का िहस्सा हूँ जो यह जानने की 
कोिशश कर रही है िक नसर् और तकनीक िमलकर लोगों को बेहतर इलाज कैसे दे सकते हैं। 
 
हम आपसे एक इंटरव्यू करना चाहते हैं िजसमें हम आपसे आपकी हाल की िक्लिनक िविज़ट के बारे में 
बात करेंगे। उस दौरान आपको एक नसर् ने देखा था जो फोन या कंप्यूटर (AI टूल) का इस्तेमाल कर रही 
थी। 
 
साक्षात्कार का उद्देश्य 
हम यह समझना चाहते हैं िक इस नए तरीके से इलाज पाने के बारे में आपको क्या अनुभव हुआ। आपकी 
राय इस तरह की देखभाल को और बेहतर बनाने में मदद करेगी। 
 
क्या होगा 
- अगर आप सहमत हैं, तो हम आपसे लगभग 30 से 45 िमनट बात करेंगे। 
- हम पूछेंगे िक आपने सलाह को िकतना समझा, आपको िकतना भरोसा और आराम महसूस हुआ, और 
क्या आप भिवष्य में इस तरह की सेवा लेना चाहेंगे। 
- आपकी अनुमित से हम बातचीत को ऑिडयो में िरकॉडर् करेंगे तािक कुछ भी छूट न जाए। 
 
स्वेच्छा से भागीदारी 
- इस इंटरव्यू में भाग लेना पूरी तरह से आपकी मजीर् पर िनभर्र है। 
- आप िकसी भी सवाल का जवाब न देने या इंटरव्यू बीच में रोकने के िलए स्वतंत्र हैं। 
- न कहने से आपकी िचिकत्सा सेवा पर कोई असर नहीं पड़ेगा। 
 
गोपनीयता और पहचान 
- आपकी पहचान िकसी भी िरपोटर् में नहीं बताई जाएगी। 
- िरकॉिडर्ंग को सुरिक्षत रूप से रखा जाएगा और केवल शोध टीम को ही इसकी पहुंच होगी। 
- अगर आपके इंटरव्यू से कोई बात िरपोटर् में रखी जाती है, तो वह िसफर् आपकी अनुमित से ही होगा। 
 
जोिखम और लाभ 
इस इंटरव्यू से कोई खतरा नहीं है, लेिकन आपकी राय से भिवष्य में इस सेवा को बेहतर िकया जा सकता 
है। 
 

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उपहार 
आपके समय के िलए हम आपको एक छोटा सा उपहार देंगे [ INR 250]। 
 
क्या आपको कोई सवाल है? 
[प्रश्नों के िलए थोड़ी देर रुकें] 
 
अगर आप तैयार हैं, तो हम आपकी सहमित लेने के िलए एक फॉमर् पर आपके हस्ताक्षर या अंगूठे का 
िनशान लेंगे। क्या आप इस इंटरव्यू में भाग लेने के िलए तैयार हैं? 

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Annexure – 7 – Consent script for nurses 
Study Title: Assessing the effectiveness of Large Language Model (LLM) - enabled 
nurse treatment planning in 2 Indian districts: A pilot study 
Principal Investigator: [Insert Full Name, Qualifications, Contact Information] 
Study Site:[Insert Health Facility Name and Address] 
You are being invited to participate in a research study being conducted by [Name of 
Institution/NGO]. This study aims to assess the effectiveness of an AI-enabled-nurse 
treatment care model for managing common chronic conditions and symptoms in a 
primary care setting in rural India. 
Before you decide, please read the following information carefully. You are welcome to 
ask questions before agreeing to participate. 
Your role and responsibilities: 
If you agree to participate, you will be expected to: 
• 
Participate in a training program about medical and AI literacy. 
• 
Do a clinical consultation with patients using the AI-supported clinical decision tool. 
• 
Allow your consultations with patients to be audio recorded. These recordings will 
help researchers study how the tool is used in real clinical interactions. 
• 
Participate in a one-on-one in-depth interview with a member of the research team. 
This interview will explore your experience using the AI tool, including what worked 
well and what challenges you faced. The interview will take approximately 45 to 60 
minutes and will be scheduled at your convenience. 
 Your identity and all personal information will be kept strictly confidential. Audio 
recordings will be securely stored and only accessible to the core research team. All 
findings will be reported in aggregate or anonymized form. If any direct quotes from your 
interview are used in research outputs (such as reports, presentations, or publications), 
they will be included only with your explicit permission. 
Your participation is entirely voluntary. You are free to decline or withdraw from the 
study at any time.  
There are no known risks associated with your participation. Your insights and feedback 
will help improve the design and implementation of digital tools for nurses in clinical 
settings. 
If you have any questions, please contact: 
Principal Investigator: [Insert Name, Phone Number, Email] 
Institutional Review Board (IRB): [Insert IRB Contact Information] 
Consent Statement 
☐ I have read and understood the information above. I have had the opportunity to ask 
questions and all my questions have been answered. I voluntarily agree to participate in 
this study, including having my consultations audio recorded and participating in an in-
depth interview. I understand that any direct quotes from my interview will only be used 
with my permission. 
Nurse’s Name: _______________________________________ 
Signature: ___________________________________________ 
Date: ___________________ 

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Annexure - 7.1 – Consent script for nurses (Bengali) 
নাস) স:িত ফম) – নাস)-এআই =ািড 
গেবষণার নাম: 
গেবষক `ধােনর নাম: [স=ূণ2 নাম ও Iযাগােযােগর তথC] 
ZাxCেকের নাম ও কানা: [এখােন িলখFন] 
আপনােক এই গেবষণায় অংশ িনেত আমণ জানােনা হে। 
এই গেবষণায় আমরা Iদখেত চাই, নাস2রা যখন Iরাগী Iদেখন, আর এক ধরেনর কি=উটার সাহাযC 
Iনয় (যােক আমরা এআই বা আ32িফিশয়াল ইেিলেজ বিল), তখন Iরাগী Iদখার ণমান Iকমন 
হয়। 
আপিন যিদ অংশ িনেত রািজ হন, তাহেল আপনােক যা করেত হেব: 
1. আপনােক এক3 `িশQণ I`াoােম অংশ িনেত হেব, Iযখােন িচিকৎসা এবং ক~ িম বFি?মbা (AI) 
স=েক2  সহজ ভাষায় Iশখােনা হেব। 
2. Iরাগীেদর িচিকৎসা Iদেবন – Iযমন আপিন কেরন, তেব এবার আপিন এক িবেশষ Iমাবাইল 
অCাপ বা কি=উটার টF ল বCবহার করেবন Iযটা িচিকৎসায় সাহাযC করেব। 
3. Iরাগীেদর সােথ আপনার কথা হেব, Iসটা Iরকড2  করা হেব (অিডও Iরকtডuং)। এেত আমরা বFঝেত 
পারব, আপিন এই টF ল3 কীভােব বCবহার করেছন। 
4. আমরা আপনােক একবার ৪৫ Iথেক ৬০ িমিনেটর এক3 সাQাৎকাের অংশ িনেত বলব। Iসখােন 
আপিন বলেবন – এই টF ল বCবহার কের আপনার Iকমন Iলেগেছ, ভােলা কী হেয়েছ, আর Iকাথায় 
অসুিবধা হেয়েছ। 
িকছH  IরJKপূণN তথ4: 
• আপনার নাম বা পিরচয় Iকাথাও `কাশ করা হেব না। 
• আপনার কথা Iরকড2  করা হেব, িকG fধF গেবষণা দেলর কেয়কজন মানুষ Iসটা fনেত পারেব। 
• আপনার ইারিভউ Iথেক যিদ Iকােনা কথা িরেপােট2  বা Iলখায় বCবহার করা হয়, তাহেল Iসটা হেব 
fধF আপনার অনুমিত িনেয়। 
• আপিন চাইেল, Iয Iকােনা সময় এই গেবষণা Iথেক সের আসেত পােরন। এেত আপনার Iকােনা 
Qিত হেব না। 
• এই গেবষণায় অংশ িনেল আপনার Iকােনা শারীিরক Qিতর আশা Iনই। 
• আপনার অিভ;তা ও মতামত ভিবষCেত এই ধরেনর টF ল ভােলাভােব বানােত সাহাযC করেব। 

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*কােনা OP থাকেল *যাগােযাগ করJন: 
গেবষণার `ধান বCিa: [নাম, Iফান নর, ইেমইল] 
পরীQা অনুেমাদন Iবাড2  (IRB): [IRB-এর Iযাগােযােগর তথC] 
স<িতর অংশ 
☐ আিম এই গেবষণা স=েক2  সব কথা বFেঝিছ। আমার যা জানার িছল, সব জানেত Iপেরিছ। আিম 
িনেজ Iথেক এই গেবষণায় অংশ িনেত রািজ আিছ। আিম জািন, আমার কথা Iরকড2  করা হেব, 
আর আমােক এক3 সাQাৎকাের অংশ িনেত বলা হেব। আমার অনুমিত ছাড়া আমার Iকােনা কথা 
Iকাথাও `কাশ করা হেব না। 
নােস2র নাম: _______________________________ 
ZাQর: ____________________________________ 
তািরখ: _______________ 
 
 

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Annexure - 7.2 – Consent script for nurses (Hindi) 
नस+ क7 सहमAत फॉम+ – नस+-AI अCययन 
अध्ययन का नाम: 
मुख्य अन्वेषक (Principal Investigator): [पूरा नाम, योग्यता, संपकर् जानकारी] 
अध्ययन स्थल: [स्वास्थ्य केंद्र का नाम और पता] 
आपको एक शोध अध्ययन में भाग लेने के िलए आमंित्रत िकया गया है। यह अध्ययन [संस्था / NGO का 
नाम] द्वारा िकया जा रहा है। इस अध्ययन का उद्देश्य ग्रामीण भारत के प्राथिमक स्वास्थ्य केंद्रों में आम 
पुरानी बीमािरयों और लक्षणों के इलाज के िलए AI (कृित्रम बुिद्धमत्ता) से मदद पाने वाले नसर् देखभाल 
मॉडल की प्रभावशीलता को समझना है। 
 
कृपया नीचे दी गई जानकारी ध्यान से पढ़ें। यिद आपके कोई प्रश्न हों तो आप पूछ सकते हैं। अध्ययन में 
भाग लेना पूरी तरह से आपकी इच्छा पर िनभर्र है। 
आपकR भूLमका और िज<मेदाJरयां: 
यिद आप इस अध्ययन में भाग लेने के िलए सहमत होते हैं, तो आपसे यह अपेक्षा की जाएगी िक आप: 
• 
आपको एक प्रिशक्षण कायर्क्रम में भाग लेना होगा, िजसमें इलाज और एआई (कृित्रम बुिद्धमत्ता) के 
बारे में जानकारी दी जाएगी। 
• 
AI (कृित्रम बुिद्धमत्ता) से सहायता पाने वाले िक्लिनकल टूल का उपयोग करते हुए मरीजों को परामशर् 
दें। 
• 
 आपकी मरीजों के साथ बातचीत को ऑिडयो िरकॉडर् िकया जाएगा। यह िरकॉिडर्ंग हमें यह समझने में 
मदद करेगी िक असल जीवन में इस टूल का उपयोग कैसे हो रहा है। 
• 
आपसे एक गहन इंटरव्यू (इन-डेप्थ इंटरव्यू) िलया जाएगा िजसमें आप AI टूल के उपयोग से जुड़े अपने 
अनुभव साझा करेंगे – जैसे िक क्या अच्छा रहा, क्या किठनाइयाँ आईं। यह इंटरव्यू लगभग 45 से 60 
िमनट का होगा और आपके सुिवधाजनक समय पर िलया जाएगा। 
आपकी पहचान और सारी व्यिक्तगत जानकारी को पूरी तरह गोपनीय रखा जाएगा। िरकॉिडर्ंग केवल 
अनुसंधान टीम के पास सुरिक्षत रूप से रखी जाएगी। अध्ययन के िनष्कषर् समूह के रूप में या गुमनाम रूप 
से साझा िकए जाएंगे। यिद आपके इंटरव्यू से कोई सीधी बात (quote) िरपोटर्, प्रस्तुित या प्रकाशन में 
उपयोग की जाती है, तो वह केवल आपकी अनुमित से ही िकया जाएगा। 
 
अध्ययन में भाग लेना पूरी तरह स्वैिच्छक है। आप कभी भी मना कर सकते हैं या बीच में भी हट सकते हैं। 
 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
54 
 
इस अध्ययन में भाग लेने से कोई ज्ञात खतरा नहीं है। आपके अनुभव और सुझाव इस तरह के िडिजटल 
टूल्स को बेहतर बनाने में मदद करेंगे। 
यिद आपके कोई प्रश्न हैं, तो कृपया संपकर् करें: 
मुख्य अन्वेषक: [नाम, फोन नंबर, ईमेल] 
संस्थागत समीक्षा बोडर् (IRB): [IRB की संपकर् जानकारी] 
सहमCत कथन 
☐ मैंने ऊपर दी गई जानकारी को पढ़ा और समझा है। मुझे अपने प्रश्न पूछने का अवसर िमला और मेरे 
सभी प्रश्नों के उत्तर दे िदए गए हैं। मैं स्वेच्छा से इस अध्ययन में भाग लेने के िलए सहमत हूं, िजसमें मेरी 
मरीजों से बातचीत की ऑिडयो िरकॉिडर्ंग और गहन इंटरव्यू शािमल है। मुझे यह भी समझ है िक मेरे 
इंटरव्यू से कोई सीधी बात केवल मेरी अनुमित से ही उपयोग की जाएगी। 
नसर् का नाम: _______________________________________ 
हस्ताक्षर: ___________________________________________ 
ितिथ: ___________________ 
 
 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
55 
 
Annexure – 8 – Consent script for doctors 
Study Title: Assessing the effectiveness of Large Language Model (LLM) - enabled 
nurse treatment planning in 2 Indian districts: A pilot study 
Principal Investigator: [Insert Name, Contact Number] 
Site: [Insert Facility Name] 
Dear Doctor, 
You are being invited to participate in a research study that aims to assess the 
effectiveness of an AI-enabled-nurse treatment care model for managing common 
chronic conditions and symptoms in a primary care setting in rural India. 
If you agree to participate: 
• 
You will see patients presenting with two chronic conditions (e.g., hypertension and 
diabetes) and three common symptoms (e.g., fever, breathlessness, and joint pain) as 
part of your routine consultations. 
• 
After each consultation, you will be asked to complete a brief proforma documenting 
key clinical details, which is expected to take 5 to 8 minutes. 
• 
The audio of your consultation with each patient will be recorded. These recordings 
will be used solely for analysis of clinical communication and decision-making. 
Your identity will be kept strictly confidential. No audio recordings will be shared 
outside the research team. The audio recordings will be retained for 5 years, as stipulated 
by national and local IRB rules and regulations.  All data will be used in aggregate form 
only. No quotes or identifying information will be used in any reports or publications. 
You will receive INR 500 per completed consultation as compensation for your time and 
participation in the study. Your participation is entirely voluntary. You may withdraw 
from the study at any time without any consequences to your professional standing or 
responsibilities. 
If you have any questions about the study, you may contact the Principal Investigator at 
[Insert Contact Number] or the local IRB at [insert contact details] 
**Consent:** 
☐ I have read and understood the purpose of the study and my role in it. I voluntarily 
agree to participate. 
Doctor’s Name: ______________________________ 
Signature: ______________________________ 
Date: ______________________________ 
 
 
 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
56 
 
Annexure – 9.1 – Budget (West Bengal) 
Exchange rate: 1 USD = INR 83.5 
Particulars 
Unit 
Unit 
cost 
($) 
Duration 
(months) 
Total 
($) 
Human resource 
 
 
 
 
Physician reimbursement @ INR 500 per 
patient 
336 
6 
 
2016 
Biostatistician 
1 
4000 
 
4000 
Research assistant 
1 
300 
6 
1800 
Sub-total 
 
 
 
7816 
Program costs 
 
 
 
 
Human-centered design workshop (local & 
outstation travel, stay, resource person fee, 
operations, per diem) 
1 
 
 
3000 
Nurses (full time hire) 
3 
250 
6 
4500 
Nurses (reserve) (0.5 FTE) 
3 
250 
2 
1500 
LLM platform membership 
4 
20 
10 
800 
Android tablets / laptops 
3 
200 
 
600 
Participant incentives 
356 
3 
 
1068 
Miscellaneous costs 
 
 
 
1000 
Sub-total 
 
 
 
12468 
Total of HR + program costs 
 
 
 
20284 
Indirect costs @5% 
 
 
 
1014.2 
Grand total 
 
 
 
21298.2 
 
Annexure – 9.2 Budget (Chhattisgarh) 
 
Particulars 
Unit Unit cost 
($) 
Duration 
(months) 
Total 
($) 
Human resource 
 
 
 
 
Physician reimbursement @ INR 500 
per patient 
336 
6 
 
2016.0 
Research associate 
1 
600 
6 
3600.0 

Assessing the effectiveness of Large Language Model 
(LLM) - enabled nurse treatment planning in 2 Indian 
districts: A pilot study 
57 
 
Subtotal 
 
 
 
5616.0 
Program costs 
 
 
 
 
Nurses (full time hire) 
3 
250 
6 
4500.0 
Nurses (reserve) @ 0.5 FTE 
3 
125 
2 
750.0 
LLM platform membership 
4 
20 
10 
800.0 
Android tablets / laptops 
3 
200 
 
600.0 
Participant incentives 
356 
3 
 
1068.0 
Miscellaneuous costs 
 
 
 
1000.0 
Subtotal 
 
 
 
8718.0 
Total HR + program costs 
 
 
 
14334.0 
Indirect costs @5% 
 
 
 
716.7 
Grand total 
 
 
 
15050.7