LETTER TO THE EDITOR

AI-Enhanced Predictive Analytics in Optimizing Patient Outcomes within Pakistan's Healthcare System.

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By Sidra Abid1, Humna Rafiq2, Maha Zehra3

AFFLIATIONS

  1. Dow University of Health Sciences, Karachi, Pakistan

DOI: https://doi.org/10.36283/PJMD13-2/019

How to cite: Abid S, Rafiq H, Zehra M. AI-Enhanced Predictive Analytics in Optimizing Patient Outcomes within Pakistan’s Healthcare System. Pak J Med Dent. 2024;13(2): 121-122. Doi: 10.36283/PJMD13-2/019

 

Dear Editor,

AI integration in healthcare, particularly in low- and middle-income countries such as Pakistan, offers revolutionary opportunities by automating processes, analyzing data, and improving healthcare efficiency while addressing staffing and financial resource challenges. Pakistan’s health and disease burden is in the bottom 25% of the world in terms of predictive analytics on outcomes for patients. The country is witnessing an increase in innovative healthcare projects that use artificial intelligence and statistical analysis to anticipate the effects on patients, influencing the future of healthcare delivery and therapeutic approaches1.

According to a study on digital health initiatives in Pakistan, 46% of the programs used technology for app development, implementation, or research, with some using AI or machine learning. These initiatives show how AI-predictive analytics can help Pakistani clinicians make better decisions and improve patients’ health. However, challenges in the health information system necessitate improvements, urging healthcare professionals to gain a better understanding of machine learning techniques2.

The main areas of concern are the size and complexity of big data, the need for novel methods, algorithms, and analytics to provide physicians with insight into the information acquired, and the moral and legal implications of developing predictive systems for analysis based on patient populations. The use of electronic algorithms to predict the likelihood of future occurrences and to continuously determine an individual’s risk score based on historical medical records and real-time data raises ethical and legal concerns. These concerns highlight the importance of a multidisciplinary strategy involving experts in law, ethics, and policy to ensure the ethical growth and application of advanced analytics systems throughout Pakistan’s healthcare sector 3.

Policymakers in Pakistan can encourage the use of AI analytics for predicting patient outcomes by implementing machine learning and data-driven strategies. Anupam Baliyan and T. Kumar’s research suggests using AI in architectural design to improve healthcare facilities, with an emphasis on improved patient care and long-term outcomes4. However, as Lavanya Kongala points out, officials must consider ethical implications to avoid bias and ensure fair AI use without being detrimental to high-risk or underprivileged populations4.

Pakistani healthcare professionals must be educated on the transformative potential of AI-powered predictive modeling for improving the health of patients in a variety of settings. AI and machine learning have shown potential in fields such as radiology and pathology, increasing diagnostic precision when compared to human experts. AI-driven healthcare systems can help to promote long-term development by enhancing facility design. However, addressing ethical concerns, particularly those related to data security and privacy, is critical to overcoming potential barriers and constraints in implementing AI in Pakistan’s healthcare system5.

ACKNOWLEDGEMENTS

None.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

AUTHORS CONTRIBUTION

All authors contributed equally.

REFERENCES

  1. Filippo, Pullara., Brian, Falkenstein., Bruce, B., Campbell., Samantha, Panakkal., Akif, Burak, Tosun., Jeffrey, L., Fine., Chakra, Chennubhotla. Abstract 5445: Segmentation-free analysis of multiplexed images with unbiased spatial analytics and explainable AI for predicting disease outcomes. Cancer Research. 2023. doi: 10.1158/1538-7445.am2023-5445
  2. Malik M, Kazi AF, Hussain A. Adoption of health technologies for effective health information system: Need of the hour for Pakistan. PLoS One. 2021;16(10):e0258081. doi: 10.1371/journal.pone.0258081.
  3. Sheng JQ, Hu PJ, Liu X, Huang TS, Chen YH. Predictive Analytics for Care and Management of Patients with Acute Diseases: Deep Learning-Based Method to Predict Crucial Complication Phenotypes. J Med Internet Res. 2021;23(2):e18372. doi: 10.2196/18372.
  4. Anupam, Baliyan T., Kumar. Predictive Intelligence for Healthcare Outcomes: An AI Architecture Overview. 2023. doi: 10.1109/ICSTSN57873.2023.10151477
  5. Rudra T. The Use of AI and Machine Learning in Healthcare and its Potential to Improve Patient Outcomes. Indian Scientific Journal of Research in Engineering and Management. 2023;7(1). doi: 10.55041/ijsrem17582

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