Impact of Patient-Generated Health Data on Personalized Medication Recommendations

DOI: https://doi.org/10.63345/ijrmp.org.v10.i6.4

Rupal Bhatt

Udaipur, Rajasthan, India

Abstract

The rapid advancement of digital health technologies has enabled the continuous collection of patient-generated health data (PGHD), offering unprecedented opportunities to tailor medication recommendations to individual needs. This manuscript investigates the impact of PGHD on personalized medication recommendations by exploring how data captured from wearable devices, mobile applications, and home monitoring systems can enhance precision in treatment protocols. Our study reviews literature up to 2020 to establish the evolution of digital data collection and its integration into clinical decision-making processes. A mixed-methods approach was adopted, combining qualitative insights from healthcare professionals with quantitative analysis of patient outcomes data from a mid-size urban healthcare center. The methodology involved the integration of diverse data sources, including self-reported symptoms, activity tracking, and physiological measurements, to refine medication dosing, timing, and therapeutic alternatives. The results indicate that PGHD, when systematically analyzed, can lead to improved treatment adherence, reduced adverse drug reactions, and enhanced overall therapeutic efficacy. However, challenges remain in data standardization, privacy concerns, and the integration of PGHD into existing electronic health record (EHR) systems. This research concludes that while PGHD holds significant promise for personalized medication recommendations, a multi-stakeholder approach—incorporating technological, clinical, and regulatory innovations—is essential to fully harness its benefits. The manuscript discusses the transformative potential of PGHD in facilitating precision medicine and provides recommendations for future research aimed at overcoming current limitations.

Keywords

Patient-Generated Health Data, Personalized Medication, Precision Medicine, Digital Health, Clinical Decision-Making, Health Informatics

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