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Article . 2022
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2022
License: CC BY
Data sources: Datacite
ZENODO
Article . 2022
License: CC BY
Data sources: Datacite
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Utilizing Pega Decisioning for Personalized Patient Communication and Dispute Resolution

Authors: Sai Kiran Nandipati; Bharat Kumar Polina;

Utilizing Pega Decisioning for Personalized Patient Communication and Dispute Resolution

Abstract

This paper explores the utilization of Pega Decisioning capabilities to personalize patient communication and resolve disputes effectively within healthcare organizations. By integrating patient data, treatment history, and dispute resolution workflows, Pega Decisioning enables tailored communication strategies and resolutions based on individual patient needs and preferences. The study employs a mixed-methods approach, combining quantitative data analysis with qualitative case studies, to assess the impact of Pega Decisioning on patient satisfaction and operational efficiency. The findings demonstrate significant improvements in personalized communication, reduced dispute resolution times, and enhanced patient engagement, suggesting that advanced decision management tools can transform healthcare communication and dispute resolution processes.

Keywords

machine learning, patient engagement, dispute resolution, personalized communication, Pega Decisioning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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