Operationalizing Machine Learning in Regulated Healthcare Systems: A Case Study of Provider Search Engine Deployment

Healthcare organizations face unique challenges when putting machine learning into practice. This article walks through the creation of a provider search tool built with AI that helps patients find the right doctors across many healthcare websites. We built it using small, independent services that work together, with special language processing tailored for medical terms. The article covers the important parts: making sure everything follows healthcare laws, keeping patient information safe, rolling out updates without breaking things, and watching the system to catch problems early. By sharing what we learned from building this in the real world, we hope other healthcare groups can follow our example while still meeting strict requirements like HIPAA and HITECH. Our experience shows that well-built AI systems can make finding providers easier while keeping patient data protected, systems running smoothly, and medical information accurate, even in sensitive healthcare settings where mistakes aren't an option.

Authors

Institutions

Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-07-16
DOI
https://doi.org/10.32996/jcsts.2025.7.7.69
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Operationalizing Machine Learning in Regulated Healthcare Systems: A Case Study of Provider Search Engine Deployment

Prashanth Kodati
Journal of Computer Science and Technology Studies
Big Data and Business Intelligence
article

Operationalizing Machine Learning in Regulated Healthcare Systems: A Case Study of Provider Search Engine Deployment

Prashanth Kodati
article en

Abstract

Healthcare organizations face unique challenges when putting machine learning into practice. This article walks through the creation of a provider search tool built with AI that helps patients find the right doctors across many healthcare websites. We built it using small, independent services that work together, with special language processing tailored for medical terms. The article covers the important parts: making sure everything follows healthcare laws, keeping patient information safe, rolling out updates without breaking things, and watching the system to catch problems early. By sharing what we learned from building this in the real world, we hope other healthcare groups can follow our example while still meeting strict requirements like HIPAA and HITECH. Our experience shows that well-built AI systems can make finding providers easier while keeping patient data protected, systems running smoothly, and medical information accurate, even in sensitive healthcare settings where mistakes aren't an option.

Journal of Computer Science and Technology StudiesVol. 7(7)
Management Sciences (United States) (US)
Openalex Percentile: Top 70%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Operationalizing Machine Learning in Regulated Healthcare Systems: A Case Study of Provider Search Engine Deployment — Prashanth Kodati · Journal of Computer Science and Technology Studies (2025) | TGRS Research Map | TGRS