This article examines the design challenges that arise when a primary care organization transitions from a commercial electronic health record (EHR) platform to a custom-built system, focusing on three interconnected dimensions: clinical workflow integration, interface usability, and structural data integrity. As of 2024, 91% of U.S. office-based physicians have adopted a certified EHR, yet EHR-related physician burnout remains associated with interface complexity, documentation burden, and coding inaccuracies. The study combines systematic literature analysis with an original engineering case at Oak Street Health to evaluate how purpose-built encounter note design, specifically the Assessment and Plan section with real-time ICD-10 search, structural CPT order linkage to diagnoses, and a pre-signing Superbill review panel,addresses these problems in practice. The proposed architecture is designed to reduce exposure to orphaned ICD or CPT codes by enforcing diagnosis-to-order binding at the application layer and to provide a structured checkpoint for identifying coding inconsistencies before claim submission. Design-stage API optimization also eliminated redundant calls, with a projected reduction of approximately 3,000 server requests per minute at peak load; this estimate requires production validation. Results are relevant for healthcare engineering teams, clinical informaticists, and health system administrators planning EHR modernization initiatives.

electronic health records, EHR migration, clinical workflow, usability

A. Aggarwal, V. Kumar and R. Gupta, "Object Detection Based Approaches in Image Classification: A Brief Overview," 2023 IEEE Guwahati Subsection Conference (GCON), Guwahati, India, 2023, pp. 1-6, doi: 10.1109/GCON58516.2023.10183609.

P. Ashwini. (2020). Study of Machine learning Algorithms for Stock Market Prediction. International Journal of Engineering Research and Technology V9. 10.17577/IJERTV9IS060064.

P. Bhawna; D. Gaurav; J. Prashant; Y. Sudeept and G. Nitin. (2021). Stock Market Prediction Using Linear Regression and SVM. In “2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)”, 629-631. 10.1109/ICACITE51222.2021.9404733.

P. Chinthan; R. Mishra; B. Prakash and B. Saleena. (2023). Stock Market Prediction Using Machine Learning Techniques: A Comparative Study. 10.1007/978-981-99-1051-9_7.

B. Prakash; P. Chinthan; B. Saleena & S. Harini. (2024). A systematic literature survey on recent trends in stock market prediction. PeerJ Computer Science 10. e1700. 10.7717/peerj-cs.1700.

Dash, A.S. & Mishra, U.. (2023). Sentiment Analysis using Machine learning for forecasting Indian stock Trend: A brief Survey. Finance: Theory and Practice 27. 136-147. 10.26794/2587-5671-2023-27-6-136-147.

D. Manju. (2022). Machine learning techniques and methodology analysis for stock market price prediction. International Journal of Computing and Artificial Intelligence 3. 13-18. 10.33545/27076571.2022.v3.i2a.50.

D. Stéphane; J. Thibault and N. Alexandra. (2022). Performance attribution of machine learning methods for stock returns prediction. The Journal of Finance and Data Science 8. 10.1016/j.jfds.2022.04.002.

H. Sepp and S. Jürgen. (1997). Long Short-term Memory. Neural Computation 9. 1735-80. 10.1162/neco.1997.9.8.1735.

L. Mei and Z. Ye. (2023). Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach. International Journal of Advanced Computer Science and Applications 14. 10.14569/IJACSA.2023.0141203.

O. Mehtabhorn; T. Nongnuch; J. Watthanasak and Wills, Gary. (2020). A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques. 10.5220/0009340700630071.

P. Ramkrishna; C. Vikas; S. Deepika; S. Ashutosh. (2021). Review of Stock Prediction Using Machine Learning Techniques 10.1109/ICOEI51242.2021.9453099.

M. Pooja; P. Sharnil; K. Ketan. (2021). Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science 7. e476. 10.7717/peerj-cs.476.

S. Gurjeet. (2022). Machine Learning Models in Stock Market Prediction. International Journal of Innovative Technology and Exploring Engineering 11. 18-28. 10.35940/ijitee.C9733.0111322.

P. Shivani; V. Shweta; S. Smriti and T. Ms. (2024). Stock Price Prediction Using Sentiment Analysis. International Journal for Research in Applied Science and Engineering Technology 12. 1301-1305. 10.22214/ijraset.2024.58167.

A. Sonali; K. Saloni; K. Anushka; K. Rohan; K. Prathamesh; K. Dhawal and K. Raj. (2023). Stock Price Prediction Website Using Linear Regression - A Machine Learning Algorithm. ITM Web of Conferences 56. 10.1051/itmconf/20235605016.

S. Yang. (2023). Research on the Stock Price Prediction Using Machine Learning. Advances in Economics, Management and Political Sciences. 22. 174-179. 10.54254/2754-1169/22/20230307.

S. Troy; R. John; R. Thomas; H. Yu-Hsiang. (2020). Machine Learning Stock Market Prediction Studies: Review and Research Directions. Journal of International Technology and Information Management 28. 63-83. 10.58729/1941-6679.1435.

V. Mehar; C. Deeksha; T. Vinay; K. Arun. (2020). Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science 167. 599-606. 10.1016/j.procs.2020.03.326.