Abstract

The challenge of integrating no-show predictive models into healthcare environments is complex and multifaceted, extending far beyond the distinct technical challenge of building accurate models. This thesis will discuss more on the subject of incorporating machine learning into healthcare. While algorithmic accuracy and model validation are critical, they are a mere sub-system of a much larger operational, technological, ethical, legal and human ecosystem. Predictive models face mass deployment in healthcare systems which are complex networks of stakeholders across strict regulatory frameworks and deeply embedded workflows (AlMuhaideb et al., 2019). However, for these models to result in long term and real improvements in the quality of healthcare delivery. For example, in the form of better resource allocation, decreased patient wait time, as well as increased system efficiency, their integration must be holistic and strategic. Just building a technically good model is not enough. Impact in the real world depends on how well the system fits into the existing healthcare structures, and how thoughtfully it is resolving those broader system level concerns. A significant portion of the complications of this integration arise from guaranteeing privacy and protection of patient data. Of all sensitive types of personal data, healthcare data has some of the strictest legal handling requirements, including the US Health Insurance Portability and Accountability Act (HIPAA) and the European Union's General Data Protection Regulation (GDPR). Because of these laws, there are strict requirements around how patient data is collected, how it is stored, how it is accessed, and how it is shared, and for any predictive system to work with patient data these requirements must be met from the beginning. In order to integrate predictive models’ healthcare providers, they require not only to comply with these regulations but also to use strong data anonymization and encryption techniques to avoid possibilities of data breaches (AlMuhaideb et al., 2019). With sensitive patient information at stake as well as the public's trust in healthcare technologies, cybersecurity measures must be robust, securing vulnerabilities at both the infrastructure and application levels. Even more important is the system interoperability issue. Significant numbers of healthcare institutions employ disparate separate electronic health record (EHR) systems, appointment scheduling platforms, and communication tools that are not well integrated and do not cooperate well with one another. With this siloed infrastructure, it then becomes very difficult to integrate in a predictive model that depends on the real- time data flow and smooth flow of patient data. For no-show prediction models to be useful, they must be incorporated into existing healthcare IT systems in a manner that facilitates real-time prediction, sends actionable outputs and is easily interpreted by clinical staff.

Library of Congress Subject Headings

Patient compliance--Forecasting--Data processing; Artificial intelligence--Medical applications; Medical appointments and schedules--Data processing; Predictive analytics; Machine learning

Publication Date

5-20-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

Download

Share

COinS