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Journal of BioData Mining

Research Article       Open Access      Peer-Reviewed

Need Assessment of the Development of an AI-integrated Personal Health Record (PHR) System for Summarizing Patient Data to Enhance Clinical Decision-making

Zakiyah Anwar*, Manish Sabharwal and Nidhi Bansal

Department of Hospital Administration, Santosh Deemed to be University, Ghaziabad, India

Author and article information

*Corresponding author: Dr. Zakiyah Anwar, Department of Hospital Administration, Santosh Deemed to be University, Ghaziabad, India, E-mail: zakiaanwar11@gmail.com
doi : 10.17352/jbdm.000002
Received: 21 October, 2025 | Accepted: 27 October, 2025 | Published: 28 October, 2025
Keywords: Personal health record; Artificial Intelligence; Health informatics; Clinical decision support; Digital health adoption

Cite this as

Anwar Z, Sabharwal M, Bansal N. Need Assessment of the Development of an AI-integrated Personal Health Record (PHR) System for Summarizing Patient Data to Enhance Clinical Decision-making. J BioData Min. 2025;1(1): 012-016. Available from: 10.17352/jbdm.000002

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© 2025 Anwar Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: The exponential growth of healthcare data presents significant challenges for clinicians and patients alike. Personal Health Record (PHR) systems, enhanced with Artificial Intelligence (AI), offer the potential to automatically summarize complex patient data, thereby improving clinical decision-making and patient engagement. However, user readiness, adoption barriers, and specific feature needs remain underexplored, especially in low- and middle-income settings.

Objective: This study aimed to assess the need, perceptions, and acceptance of an AI-integrated electronic PHR system designed to summarize patient data and enhance clinical workflows, from the perspectives of healthcare professionals and patients.

Methods: A cross-sectional survey was conducted among 384 participants (195 healthcare workers and 188 patients) across a multi-specialty healthcare network in India. Validated questionnaires measured current health record management challenges, awareness of digital health initiatives like the Ayushman Bharat Health Account (ABHA), and preferences and concerns related to AI-enabled PHR adoption. Descriptive and inferential statistical analyses evaluated user readiness and feature prioritization.

Findings: While smartphone ownership reached 100% among patients, traditional paper records remain prevalent (74%). Both patients and healthcare workers reported critical issues with data fragmentation, record loss, duplicate testing, and administrative burden. Awareness of ABHA was high among professionals (89%) but limited in patients (26%), with usage below 6% in both groups. Despite this, over 90% expressed a strong willingness to adopt AI-supported PHR solutions, emphasizing automated summarization, secure digital lockers, and mobile accessibility. Privacy, data accuracy, and training emerged as primary concerns.

Interpretation: These findings reveal a pressing need and promising acceptance for AI-integrated PHR systems that address key pain points in health data management. To optimize adoption, future system development must prioritize user-centered design, robust privacy safeguards, explainable AI, and integration within national digital health frameworks.

Indexing and Abstracting

Introduction

The rapid advancements in information technologies have revolutionized healthcare delivery worldwide. Patient data has become voluminous and complex, requiring innovative tools to assist both healthcare providers and patients in managing and interpreting this information effectively. Personal Health Records (PHRs) empower patients with control over their health data and facilitate seamless information exchange, ideally fostering patient engagement and improved health outcomes. Yet, current PHR systems are limited by fragmented data, difficult navigation, and information overload, limiting their clinical utility.

Artificial Intelligence (AI) presents transformative potential-especially through natural language processing and machine learning summarize and highlight essential patient information within PHRs. Such AI integration can catalyze faster, more accurate clinical decisions and enhance patient understanding. However, the successful development and adoption of AI-integrated PHR systems hinge on comprehensively understanding the needs and concerns of both clinicians and patients, particularly in diverse healthcare environments such as India.

This study investigates the current challenges in health record management, awareness of digital health initiatives, and readiness to adopt AI-based PHR advancements among Indian healthcare professionals and patients. Insights from this research aim to inform the design and implementation of next-generation PHR systems tailored to user preferences and constraints [1-3].

Methods

Study design and setting

We conducted a descriptive cross-sectional study across tertiary hospitals, outpatient clinics, and primary care centers in and around Ghaziabad, India, between June 2023 and May 2024. Using stratified random sampling, we recruited 195 healthcare workers (doctors, nurses, administrators) and 188 patients with prior exposure to health records.

Data collection

Validated self-administered questionnaires, available in English and Hindi, captured demographic data, current health record management practices, digital access, AI awareness, willingness to adopt AI-integrated PHRs, feature preferences, and privacy concerns. Ethical approval was obtained, and informed consent was ensured.

Target population

Patients and healthcare workers from tertiary care hospitals and clinics in Ghaziabad, India.

Inclusion criteria:

  • Patients aged 18 years and above with at least one prior hospital visit.
  • Healthcare professionals (doctors, nurses, or administrators) with more than six months of professional experience.

Exclusion criteria:

  • Patients are unwilling to provide informed consent.
  • Healthcare workers without direct involvement in patient care or medical record handling.

Data analysis

Data were entered and analyzed using SPSS software (version 26.0). Descriptive and inferential statistics were applied. A Chi-square test revealed a significant association between ABHA awareness and AI-integrated PHR readiness (p < 0.05), confirming statistical significance.

Statistical analysis

Data were analyzed using SPSS v28. Descriptive statistics summarized participant characteristics and response patterns. Pearson’s chi-square tests assessed associations between variables such as respondent type, age, education, ABHA awareness, and AI readiness. Statistical significance was set at p < 0.05. Reliability testing yielded Cronbach’s alpha of 0.82.

Results

Participant demographics

The patient group had 57% females and 43% males, predominantly young adults aged 18–30 years (77.4%) and well educated (87% with college-level education or higher) (Tables 1-3).

Table 1: Gender distribution among patients.
Gender Percentage (%)
Female 57
Male 43
Table 2: Age group distribution of patients.
Age Group Percentage (%)
18–30 77.4
31–45 17.4
46–60 4.3
<18 0.9
Table 3: Education level of patients.
Education Level Percentage (%)
Higher Education 87
Postgraduate 4
Medical 3
No Formal Education 3
Graduate 2
Undergraduate 1

Among healthcare workers, 42% were doctors, 37% nurses, 8% administrators, and 13% other support staff.

Nearly 45% had less than 2 years of experience, indicating a relatively young workforce open to digital innovation (Tables 4,5).

Table 4: Profession distribution among healthcare workers.
Profession Percentage (%)
Doctors 42
Nurses 37
Administrators 8
Others 13
Table 5: Experience group of healthcare workers.
Experience Group Percentage (%)
< 2 years 45
2–5 years 22
6–10 years 18
11–20 years 10
> 20 years 5

Technology access and usage

All patients owned smartphones (100%), and 93% expressed willingness to access their health records via mobile devices. However, 74% still stored medical records in physical paper form (Tables 6-8) [4-7].

Table 6: Smartphone ownership among patients.
Ownership Percentage (%)
Yes 100
No 0
Table 7: Willingness of patients to access health records via mobile.
Willingness Percentage (%)
Yes 93
No 7
Table 8: Current medical record storage patterns among patients.
Storage Method Percentage (%)
Paper files 74
Smartphone/Computer 17
Healthcare Providers’ Records 9

Challenges in health record management

Over 60% of participants reported having experienced or witnessed clinical delays caused by missing or incomplete patient records. Duplicate testing due to unavailable prior results was reported by 62% of patients. Healthcare workers noted excessive paperwork and difficulty retrieving comprehensive patient histories, adversely affecting care efficiency (Tables 9,10) [8-10].

Table 9: Record loss leading to care delays.
Group Percentage (%)
Patients 63
Healthcare Workers 94
Table 10: Duplicate tests due to unavailable previous reports.
Group Percentage (%)
Patients 62
Healthcare Workers 96

Awareness and usage of digital health platforms

High awareness of the Ayushman Bharat Health Account (ABHA) was recorded among healthcare professionals.

(89%), contrasted with only 26% patient awareness. Actual usage of ABHA remained low (<6%) in both groups (Table 11).

Table 11: Awareness and usage of ayushman bharat health account (abha).
Group Awareness (%) Usage (%)
Patients 26 5
Healthcare Workers 89 Negligible

Readiness to adopt ai-integrated phrs

More than 90% of both healthcare professionals and patients indicated willingness to adopt AI-powered PHR systems. Desired features included automated patient history summarization (77%) and real-time alerts (65%).

Primary concerns centered on data privacy (38%) and accuracy of AI outputs (29%) [11-16].

Ethical approval and data privacy

Ethical clearance for this study was obtained from the Institutional Ethics Committee of Santosh Deemed to be University (Approval No: SU/2025/CRF/279). Participation was voluntary, and all respondents provided informed consent. Data were anonymized, securely stored, and used exclusively for research purposes in compliance with institutional and data protection guidelines [17-26].

Discussion

This study highlights substantive gaps in current patient data management, particularly record fragmentation and workflow inefficiencies, which contribute to diagnostic delays and redundant testing. Coupled with the widespread use of smartphones and strong openness to AI integration, these findings indicate fertile ground for deploying smart PHR systems in India [27-35].

Despite established digital infrastructures like ABHA, low patient awareness and limited actual use point to systemic barriers, including inadequate outreach, insufficient integration with clinical workflows, and a lack of user training. Bridging this awareness-adoption gap is critical [36-45].

The strong preference for AI-powered summarization underscores the potential for technology to alleviate clinician cognitive overload and improve patient comprehension. However, privacy and accuracy concerns warrant transparent AI design and robust security frameworks [46-51].

Strengths and limitations

The study’s comprehensive dual-perspective approach and robust sample size enhance the relevance of findings. Limitations include reliance on self-reported data and confinement to a single geographic region, which may affect generalizability. Future work should pilot AI-integrated PHR prototypes and evaluate clinical outcomes.

Conclusion

There is a clear need and readiness for AI-integrated Personal Health Records in the Indian healthcare context to enhance data accessibility, clinical decision-making, and patient engagement. Successful implementation will require addressing privacy concerns, raising awareness, involving end-users in design, and aligning with national digital health strategies. This study provides critical user-informed insights to guide the development of intelligent, secure, and user-centered PHR systems.

Author contributions

Dr. Zakiyah Anwar – Conceptualization, study design, data collection, and manuscript preparation.

Dr. Manish Sabharwal – Supervision, review, and critical feedback.

Dr. Nidhi Bansal – Methodological guidance, validation, and data interpretation.

Acknowledgments

I express my heartfelt gratitude to my guide, Dr. Manish Sabharwal, for his constant guidance and support throughout this study. My sincere thanks to Dr. Nidhi Bansal for her insightful suggestions and encouragement. I also thank Dr. Shalabh Gupta, Professor and Dean of Academics, for his valuable input and motivation.

I am deeply thankful to all the participants who took part in this study. I owe immense gratitude to my parents, Mr. Anwar Ahmad and Mrs. Hashmeem Naaz, my brother, Mr. Ali Haider, and my siblings for their unwavering love and encouragement. A special thanks to my husband, Mr. Mohammad Shiraz, for his patience, understanding, and constant motivation.

Finally, I thank God Almighty for His blessings and guidance throughout this research journey.

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