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Using Artificial Intelligence to Enable Patient-Driven Healthcare
In a world driven by social media and online forums, healthcare systems have an opportunity to identify and act on very specific insights directly from patients.
Heka by Sia Partners’ DeepReview: Patient Sentiment Analysis in Healthcare
In a world driven by social media and online forums, healthcare systems have an opportunity to identify and act on very specific insights directly from patients. Sia Partners has taken important learnings from other industries that have capitalized on customer sentiment analysis and applied the same principles to analyze the sentiments of patients within a healthcare system. The use of patient sentiment analysis to analyze patient opinions can be used to track patients’ unique and specific thoughts and experience about a healthcare service, product, medication, and more.
Tracking and analyzing patient feedback can and should be a crucial input into decision making for healthcare organizations providing necessary insights to drive innovation.
Patient sentiment analysis is a form of text analytics that determines the attitude that patients have towards aspects of a service or product by extracting the sentiment of these patients’ feedback from various online platforms such as Google reviews, Healthgrades, and ZocDoc. Sentiment analysis uses natural language processing (NLP) to score each piece of text in a patient review based on the sentiment of the words and phrases. On a broad scale, sentiment analysis is beneficial because it:
- Can manage large volumes of patient insights data better than humans;
- Reduce reliance on human intuition, which tends to be riddled with biases;
- Can synthesize findings quickly.
What is Natural Language Processing?
NLP is a branch of Artificial Intelligence (AI) that provides computers the ability to understand text and spoken words similar to the way humans can. This technology combines computational linguistics - the foundation for rule-based models of human language - machine learning, and deep learning models.
Current sentiment analysis uses machine learning to determine the meaning behind patient reviews to account for the subtleties in human language. It recognizes the sentiment behind words based on a labelled training set. Continuous innovation has made it possible for sentiment analysis to take into account sentiments based on polarity, emotions, intentions, and urgency.
Heka by Sia Partners’ Sentiment Analysis Tool: DeepReview
Sia’s DeepReview tool leverages Natural Language Processing to analyze patient reviews, metadata, business data, socio-demographic data, and affiliate data to generate indicators that can be applied to networks and geographic areas. Using AI, DeepReview collects comments from various opinion sources; this data often remains untapped as its consolidation is complex and time-consuming. DeepReview drives incredible insight with real time tracking and understanding, communicating the main themes that impact patients’ experience.
This Automatic Natural Language Processing (ANLP) and sentiment analysis also allows for attribution of comments to a specific topic or strategy. Users of the DeepReview tool can specify keywords and topics to focus on, such as patient adherence, physician manner, or waiting time. On the DeepReview dashboard (Figure 1), users can look at general data, narrow down based on location or establishment type, and even segment reviews by theme or characteristics (such as service, staff, wait time, other processes, and positive or negative).
Figure 1 - DeepReview Dashboard
DeepReview Case Study: Improving Patient Adherence
Patient adherence is vital for improving patient outcomes, preventing unnecessary complications, and optimizing hospital costs.[1] According to a 2018 NIH study, up to 50% of patients treating chronic conditions fail to adhere to their medication routines. This lack of adherence ultimately has lead to over 100,000 avoidable deaths and $100 billion in unnecessary annual medical costs.[2] It is estimated that 69% of hospital admits are a result of non-adherence to prescribed medicine [3]
In order to prevent unnecessary costs and patient readmissions, an investment in patient sentiment analysis can be used to help healthcare providers and hospital systems understand what is driving a lack of patient adherence. This investment will ultimately provide healthcare clinicians with insights to prevent readmissions and reduce avoidable complications.
The costs of not implementing patient sentiment analysis can be high. Healthcare providers can risk poor patient retention, unnecessary readmission costs, and Medicare reductions.
By aggregating reviews and tracking satisfaction and dissatisfaction, healthcare providers, hospitals, and healthcare systems can identify effective processes and changes from ineffective -- those lacking adoption or satisfaction with patients. Understanding where pain points exist for patients enables healthcare providers to focus their efforts on value-add; adjusting to meet patient needs and expectations. DeepReview data gives healthcare the power to improve not only patient adherence, but also to improve patient outcomes, and optimize healthcare costs -- all while truly giving a voice to those who matter most, the patients.
How Sia Partners Can Help
Coupled with the DeepReview tool, Sia Partners offers Healthcare & Life Science consultants and subject matter experts who advise and support healthcare providers in navigating their Sentiment Analysis data, as well as strategizing and executing the change and communication plans needed to impact patient sentiment. Sia Partners is a next-generation consulting firm, not only providing traditional management consulting in areas such as Project/Program Management, Process Improvement, and Change Management, but also bringing Innovation, Analytics, Technology Advancements and Design to our client delivery.
For more information on DeepReview, check out this article. To learn more about Sia Partners, please visit sia-partners.com.
