In this special guest feature, Senem Guney, PhD, CPXP, Founder and Chief Experience Officer at NarrativeDx, discusses why hospitals need big data to improve patient experience. Hospital administrators seeking to provide excellent patient experiences need to understand responding to patients as a big data problem and arm themselves with the necessary technology solutions. Senem received her PhD in Organizational Communication & Technology at The University of Texas at Austin. Her dissertation was based on her work as a process consultant at the IBM Austin Hardware Lab. She was on the faculty of the College of Computing and Information at SUNY, Albany. She was also a Fellow at the Center for Technology in Government, a SUNY-Albany affiliated think tank. Her research and consultancy experience spans across healthcare, technology development, and state government organizations.
Hospitals are businesses, too. That fact may be obfuscated when you are a patient simply looking for care, but behind the scenes there are administrators, marketers, lawyers, and every other piece of the puzzle needed to run an effective, profitable business. As a result, unhappy patients are expensive for hospitals, and they care about what patients think of their experience, because if you go on Yelp and write something negative about your recent hospital visit, that hurts their bottom line.
Just like any other business, hospitals want good Yelp reviews. But for hospitals, improving their ratings online is not as easy as restaurants might be able to do by serving customers faster or using fresher ingredients.
Hospitals usually interface with people at very low moments in their life. That is because their clients are also patients who have maladies that are causing them physical or emotional pain. Trying to create a good patient experience for someone who does not even want to be there is a challenge.
New technology can help hospitals tackle the patient experience challenge. The key is to understand what factors influence the perceived quality of a hospital visit. At the moment, the process for collecting this information is analogue at best, through what is called the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. This survey is required by the Centers for Medicare and Medicaid Services – a federal government agency that oversees many healthcare programs and reimburses healthcare providers for their services. Hospitals that score well on the HCAHPS survey get bonuses from CMS. Hospitals that don’t can have substantial sums of Medicare reimbursements withheld.
The problem with using a survey is that it limits context to answers. If a standardized survey question asks, “Was your nursing staff responsive and attentive to your needs?” it forces the respondent into a predetermined answer that lacks detail. By contrast, a patient who writes a review on Yelp may comment that the hospital staff was attentive but did a poor job of explaining their conditions or treatment options, how much longer they had to stay, or how to use the drugs they were given.
That is where natural language processing (NLP) and artificial intelligence (AI) can help. These tools can find relevant commentary about patient experience across the web. A hospital that wants richer data about how their patients perceived their stay at the hospital should care as much or more about a review left on a consumer site or internet forum, as they do about answers to the HCAHPS survey.
Patient reviews on internet forums are valuable for a number of reasons. For starters, they are public, and as a result, it is in any hospital’s best interest to address issues that are being talked about. But beyond that, the internet is a medium in which people are comfortable being brutally honest, which means the feedback is less likely to be filtered. And while there are many more reasons, I’ll add just one more – commentary online can be treated like big data and can be aggregated to extract actionable insights for hospital administrators to use.
By treating patient experience feedback as big data, you are able to analyze sentiments associated with this feedback and identify patterns. For example, a patient’s review may start with remarks about interactions with the nursing staff. If you had the capability to analyze the review in its entirety and categorized it with other comments that contained similar themes, you would find out that the thing that really frustrated the patients in their interactions with nurses was the lack of warm blankets at night. Utilizing NLP and AI, these patterns can be connected and surfaced for administrators to take targeted improvement actions.
Because the internet has made consumers more powerful than ever, the requirement for hospitals to be more responsive to their patients’ needs and expectations has increased. Hospital administrators seeking to provide excellent patient experiences need to understand responding to patients as a big data problem and arm themselves with the necessary technology solutions. The impact this can have on a hospital’s bottom line is significant and anyone behind the adoption curve is likely to experience that impact in a negative way.
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