Use Big Data to Improve the Patient Experience

by   |   October 27, 2015 5:30 am   |   0 Comments

Historically, patient experience as it relates to outcomes largely has been neglected, mainly because hospitals believed that they needed to connect unemotionally and objectively with patients in order to treat them effectively. But hospitals are realizing that patient treatment goes beyond physical ailments. It also involves how the patient feels emotionally about factors such as staff behavior, facial expressions, and approachability; the cleanliness of the facility; the billing experience; and the empathy they experience in the hospital. Such factors constitute patient experience, and hospitals are trying to quantify patient experience with big data.

A patient’s experience with a hospital is about more than purely the quality of medical treatment. It also encompasses the overall emotional experience, which includes ease of scheduling appointments, the approachability and friendliness of staff, timeliness, empathy, and cleanliness. The image below shows the broad range of factors that can influence a patient’s experience.

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Click to enlarge


Data about the patient experience can be sourced from different sources, as shown in the image below.

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Click to enlarge


Here are five ways big data can improve the patient experience and outcomes.

The Cleveland Clinic Way

The Cleveland Clinic significantly improved its patient experience with the help of analytics. In 2009, the clinic had not been receiving high scores on the patient satisfaction index. The CEO, Dr. Delos Cosgrove, decided to use analytics to improve patient services. To turn the vision into reality, the clinic hired Dr. James Merlino as the Chief Experience Officer. Dr. Merlino hired a third-party agency to conduct a quantitative and qualitative study on what the patients expected from the clinic. The findings from the analytics were different from what the clinic thought the patients expected from the clinic. The patients, the analytics revealed, expected respect, clear and consistent communication, and happy hospital staff. These expectations were connected with the emotional state of the patients. Patients wanted to feel concern and empathy from hospital staff.

Gaining Insights from Big Data

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Gaining insights is the first important step toward improving the patient experience, and there are a lot of ways to do that. For example, social media and website discussions could reveal that patients tend to feel a lot of anger at the billing inefficiencies of a particular hospital. Advanced analytics could quantify the range of emotions. Analytics engines could search websites such as Twitter to identify trending topics on healthcare and analyze the content. It is important to identify the most important issues in the minds of the patients – it could be availability of parking spaces, lack of clarity in communication, unclean bathrooms, and even chaotic billing counters.

The idea is to identify the trending topics and assign ratings to them. For example, positive sentiments could be awarded green colors and negative sentiments could be given red colors. Advanced analytics is capable of generating reliable ratings from this data. This data also could provide hospitals with valuable information to create Key Performance Indicators (KPI). The advantage with this approach of gathering and analyzing data is that it happens relatively quickly compared to the traditional method of surveys.

Creating Action Plans and Goals

After identifying the trending or hot topics, the next step is to identify a set of variables that are playing a role in patient dissatisfaction. In this context, examples of variables could be billing errors, waiting time at the investigation departments, lack of process, poor attitudes toward patients, or difficulty in scheduling appointments.

After the variables are identified, the hospital can decide what constitutes an acceptable change in variable values. For example, the hospital could target to limit billing errors to 1 percent of the total number of bills generated in a month. Data science is also capable of reasonably estimating the impact of the change in variable values on the overall patient experience.

Reducing Readmissions

According to Paul Muller, Chief Software Evangelist at HP, hospital admissions in the United States constitute about 30 percent of the total annual healthcare cost and 20 percent of all hospital admissions happen within 30 days of a previous discharge. Muller observed, “In other words, we are potentially letting people go without having completely resolved their issues. Better utilizing big data technology can have a very real impact, for example, on the healthcare outcomes of your loved ones.”

The key to big data helping reduce hospital readmissions lies in accessing and analyzing medical and health data of the patients and developing plans accordingly. If a patient is readmitted within 30 days, something probably has gone wrong with the post-discharge care. So, accurate analysis needs to be done on the possible risks, actions, emergency situations, medications, history of illnesses, and so on.

Reducing Avoidable Expenses

According to Muller, medical errors are one of the biggest contributors to avoidable medical expenses in the United States and could be as high as 17.6 percent of the GDP. Medical errors such as a sponge left in the stomach after operation or an overdose leading to infection could drive up the costs and insurance expenses that hospitals incur. Inefficiencies also lead to higher costs. In addition, medical errors and inefficiencies contribute substantially to patient dissatisfaction and reduce patient outcome scores. Big data analytics, combined with the right technology, can expose the problems in an objective manner.

Patient experience has been a neglected component of treatment for a long time but it is finally getting the attention it deserves. The challenge is to create an action plan based on patient feedback data and put it into practice.

Kaushik Pal has more than 16 years of experience as a technical architect and software consultant in enterprise application and product development. He is interested in new technology and innovation areas, as well as technical writing. His main focus area is web architecture, web technologies, Java/J2EE, Open source, big data, cloud, and mobile technologies. You can find more of his work at Email him at

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