During the Q&A portion of my talk last week at the Digital Health Summer Summit, I was asked if patients always write negative comments about meals.
While it’d be a sweeping generalization to say patients always write negatively about meals, one area of patient experience that is persistently hard to improve is the food in hospitals. There is no specific measure for meals on the HCAHPS survey; but there is no dispute in the industry that meals are a significant component of a patient’s hospital stay, and no one likes hospital food.
NarrativeDx clients cover the whole spectrum of healthcare provider organizations. We have implementations at urban hospitals with 1,000+ beds, and also serve rural hospitals with fewer than 50 beds.
At a 40-bed hospital in rural Texas, we found - as we commonly do - that a large number (43%) of comments about meals from patient satisfaction surveys were negative. Word gets around very quickly in a small town, and the hospital’s patient experience officer was concerned about their reputation in the community for some of their patient experience areas that consistently received low scores. After analyzing verbatim feedback from patients, NarrativeDx quickly detected a negative trend about how vegetables looked and tasted.
When you conduct surveys, you learn how your patients feel about your meals (among many other things). When you bring in the capability to display trends generated from your patients’ comments, you’re able to discover the exact reason why they do or do not like your meals. In this case, the specific issue with the meals was overcooked vegetables.
In our first customer experience call post-implementation, we went over the dashboard with the hospital’s patient experience officer. We showed her the drill-through feature for categories that had come up to the surface as improvement opportunities; that is, the largest categories of comments that were trending negatively. One of those opportunities for improvement was “Meal Quality.” The drill-through feature in our dashboards displays the discharge date, unit, age, ethnicity, gender, and major category for each comment - trending negatively or positively - as you can see in the image below.