Five Data-Driven Strategies That Lower Costs, Reduce Incidents, and Cut Readmission Rates

Historically, patients’ treatment experience as a therapeutic outcome has been largely overlooked. This phenomenon is primarily attributable to the prevailing view among physicians that their principal responsibility is to treat patients objectively and effectively. However, clinicians are increasingly recognizing that the process of caring for patients differs substantially from merely treating the disease itself. The patient care process should encompass measures that convey emotional support, such as healthcare providers’ verbal and nonverbal communication, facial expressions, clean and well-maintained facilities, itemized hospital billing statements, and physicians’ practice of perspective-taking. Together, these elements constitute the patient’s treatment experience.

Meanwhile, hospitals are also leveraging big data to quantify patient experience. Data indicates that the hospital experience encompasses not only ensuring clinical efficacy but also a holistic emotional experience, including factors such as convenient and pleasant appointment scheduling, courteous medical staff, punctuality, empathy toward patients, and cleanliness. The figure below illustrates the numerous factors influencing patients’ treatment experience.


Patient experience data comes from various sources, as shown in the figure below.



Five Ways to Enhance Patient Treatment Experience and Outcomes Using Big Data

Cleveland Clinic Model
Cleveland Clinic Has Significantly Enhanced Patient Treatment Experience Through Data Analytics. As early as 2009, Cleveland Clinic did not score highly on patient satisfaction indices. Dr. Delos Cosgrove, the clinic’s CEO, decided to leverage data analytics to improve patient care services. To turn this vision into reality, the clinic appointed Dr. James Merlino as Chief Experience Officer. Dr. Merlino engaged a third-party agency to conduct qualitative and quantitative data analyses, aiming to identify patients’ true expectations and demands regarding clinic services. The analytical results revealed substantial discrepancies between actual patient needs and the clinic’s prior assumptions. Data analytics indicated that patients desired to be treated with respect, to have clear and consistent communication with their physicians, and to see cheerful medical staff. These expectations were closely linked to patients’ emotional states. Patients sought care and empathy from healthcare providers.

Gaining Insights from Big Data
Gaining insights is the first critical step toward enhancing the patient experience, and there are many ways to achieve this. For example, reviews on social media platforms and websites have revealed that patients are increasingly complaining about billing inefficiencies at certain hospitals. Advanced analytics can determine the spectrum of patient sentiment. Analytical engines can scour the web—such as searching sites like Twitter—to identify trending topics in healthcare and analyze the content. Most importantly, it is essential to pinpoint what matters most to patients, which could be the availability of hospital parking, lack of clear communication with physicians, unclean hospital restrooms, or even erroneous billing charges.

The objective is to identify trends in trending topics and assign them ratings. For instance, positive sentiment can be designated as green, while negative sentiment can be marked as red. Advanced analytics can generate authentic and credible ratings based on the data itself. This data can also provide healthcare institutions with valuable insights for establishing key performance indicators (KPIs). Compared to traditional survey methods, this approach enables faster data collection and analysis.

Create Action Plans and Goals
After identifying trends and hot topics, the next step is to determine a set of variable data that contributes to patient dissatisfaction. For example, these variables may include billing errors, excessive wait times in diagnostic departments, lack of standardized procedures, indifferent staff attitudes toward patients, or difficulties in scheduling appointments. Once this set of variables is identified, hospitals can define acceptable ranges for these metrics. For instance, a hospital might set a target to keep billing errors within 1% of total billings per month. Data science can also reasonably assess how changes in these variable values impact the overall patient experience.

Reducing Readmission Rates
According to Paul Muller, HP’s Chief Software Marketing Officer, inpatient hospital costs in the United States account for approximately 30% of total annual healthcare expenditures, and 20% of hospital admissions occur within 30 days of discharge.

Mueller stated, “In other words, we may be discharging patients without fully resolving their medical conditions. Better utilization of big data technologies can yield a very tangible impact, such as providing health outcome data on your loved ones.”

The key to leveraging big data to reduce readmission rates lies in accessing and analyzing patients’ medical and health data, and formulating corresponding treatment plans. If a patient is readmitted within 30 days, it may indicate issues with post-discharge care. Therefore, accurate analysis should be conducted on potential risks, activities, emergencies, medication regimens, medical history, and other relevant factors for each patient.

Reduce Unnecessary Expenditures
Mueller stated that medical malpractice is one of the largest contributors to avoidable healthcare costs in the United States, which may amount to 17.6% of the U.S. GDP. Examples of medical malpractice include leaving surgical sponges in a patient’s stomach after surgery or causing infections due to medication overdoses; such incidents increase both treatment costs borne by hospitals and insurance premiums. Inefficiency also leads to higher expenditures. Furthermore, medical malpractice and operational inefficiencies contribute to patient dissatisfaction, thereby lowering hospital ratings. Big data analytics, when combined with appropriate technology, can more objectively reveal existing problems within hospitals.

For a long time, patients’ treatment experiences were overlooked; however, patient experience has ultimately received the attention and recognition it deserves. The challenge we face is to develop an action plan based on patient feedback data and put it into practice.

Original Author of This ArticleKaushik Pal has over 16 years of consulting experience in enterprise applications and product development.

Compiled by Chen Kun

Editor: Huang Jia