Last week, I wrote about the data that the Federal government is giving away for free. Their intent is to encourage entrepreneurs and developers to build new and innovative products and services. I highly recommend you check out the data.gov site for data in such areas as energy and education and safety. After doing several searches on the data.gov site, I found one database that included customer survey data. These customer survey data were specific to the healthcare industry and reflected patient ratings of hospital quality. In this week’s post, I will analyze these patient ratings to understand how to improve the patient experience. First, let’s review the survey itself.
The Survey of Patients’ Hospital Experience
This survey is known as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems). HCAHPS (pronounced “H-caps“) is a national, standardized survey of hospital patients and was developed by a partnership of public and private organizations.
The development of HCAHPS was funded by the Federal government, specifically the Centers for Medicare & Medicaid Services (CMS) and the Agency for Healthcare Research and Quality (AHRQ). HCAHPS was created to publicly report the patient’s perspective of hospital care.
The data.gov site indicates that the data files were updated in 5/30/2012. Based on HCAHPS reporting schedule, it appears the current survey data were collected from Q3 2010 through Q2 2011 and represent the latest publicly available patient survey data.
The survey asks a random sample of recently discharged patients about important aspects of their hospital experience. The data set includes patient survey results for over 3800 US hospitals on ten measures of patients’ perspectives of care. The 10 measures are:
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- Nurses communicate well
- Doctors communicate well
- Received help as soon as they wanted (Responsive)
- Pain well controlled
- Staff explain medicines before giving to patients
- Room and bathroom are clean
- Area around room is quiet at night
- Given information about what to do during recovery at home
- Overall hospital rating
- Recommend hospital to friends and family (Recommend)
For questions 1 through 7, respondents were asked to provide frequency ratings about the occurrence of each attribute (Never, Sometimes, Usually, Always). For question 8, respondents were provided a Y/N option. For question 9, respondents were asked to provide an overall rating of the hospital on a scale from 0 (Worst hospital possible) to 10 (Best hospital possible). For question 10, respondents were asked to provide their likelihood of recommending the hospital (Definitely no, Probably no, Probably yes, Definitely yes).
The data set reported metrics for each hospital as percentages of responses. Because the data set had already been somewhat aggregated (e.g., percentages reported for group of response options), I was unable to calculate average scores for each hospital. Instead, I used top box scores as the metric of patient experience. I found that top box scores are highly correlated with average scores across groups of companies, suggesting that these two metrics tell us the same thing about the companies (in our case, hospitals).
Top box scores for the respective rating scales are defined as: 1) Percent of patients who reported “Always”; 2) Percent of patients who reported “Yes”; 3) Percent of patients who gave a rating of 9 or 10; 4) Percent of patients who said “Definitely yes.”
Top box scores provide an easy-to-understand way of communicating the survey results for different types of scales. Even though there are four different rating scales for the survey questions, using a top box reporting method puts all metrics on the same numeric scale. Across all 10 metrics, hospital scores can range from 0 (bad) to 100 (good).
The descriptive statistics of and correlations among the metrics are located in Table 1. As you can see in the table, all correlations among the metrics are statistically significant, indicating that hospitals tend to receive consistent ratings across different metrics; that is, some hospitals tend to get high ratings across all metrics and other hospitals tend to get low ratings across all metrics.
Loyalty Driver Analysis
Loyalty Driver Analysis is a business intelligence solution that helps companies understand and improve customer loyalty, an indicator of future business growth. In loyalty driver analysis, we look at two key pieces of information of each business area: 1) Performance of the business area and 2) Impact of that business area on customer loyalty. The results are typically presented in a driver matrix (see Figure 1).
Using these two key pieces of information for each business area, senior executives are able to make better business decisions by identifying those business areas that 1) have low performance and 2) have a big impact on customer loyalty (upper left quadrant of Figure 1). Improving these business areas will necessarily lead to improved customer loyalty.
For the current analysis, I created a patient loyalty metric by averaging the two general survey questions (Overall hospital quality rating and Recommend hospital). This composite score, I call the Patient Advocacy Index (PAI), has a reliability estimate (Cronbach’s alpha) of .95. The PAI was then correlated with each of the remaining eight survey questions to understand how much of an impact each business area (e.g., nurse communication, responsive) has on patient advocacy.
The driver matrix using the patient survey data appear in Figure 2. We see that there are three key drivers of patient advocacy: 1) Staff explains meds, 2) Responsiveness and 3) Pain well controlled. These areas appear in the upper left quadrant and suggest that these areas are important to patient advocacy and have much room for improvement
There are a few important points we can conclude based on the analyses:
- The biggest driver of patient advocacy is the patients’ perception of the quality of nurses’ communication effectiveness. Because nurses are likely involved with most of the day-to-day dealings with patient care, their performance impacts many different facets of the patient experience (e.g., Responsiveness, staff explains med).
- To improve patient loyalty toward hospitals, the hospital industry might consider focusing on three areas: 1) Pain management, 2) Responsiveness and 3) Staff explaining meds to patients. As an industry, these three patient experience areas appear as key drivers of patient loyalty; that is, each has much room for improvement and has a relatively big impact on patient advocacy.
- The quality of doctor communication is the second lowest driver of patient advocacy. While doctor communication quality is still important to patient advocacy (r = .56 with patient advocacy), it is less important than other patient experience areas like cleanliness of the patients’ rooms, responsiveness (getting help when needed), and getting information about their home recovery. Doctors’ involvement might be perceived as less important to the patients simply because the patients have less exposure to doctors, especially when compared to the patient’s exposure to nurses.
- Patient Advocacy Index (PAI) appears to be a reliable, valid metric of patient loyalty. This two-item scale has high reliability and is related logically to other patient experience metrics. While the PAI is a good metric of patient advocacy, the hospital industry might consider examining other types of ways that patients can demonstrate their loyalty toward their hospital. In my research, I have found that there are three general types of customer loyalty (e.g., advocacy, purchasing and retention), each responsible for different types of business outcomes. Perhaps hospitals need to expand their idea regarding patient loyalty and develop measures that reliably tap different ways patients can show their loyalty towards hospitals.
The concept of customer experience management (and all its trappings) can be applied to the healthcare industry. Using existing nationwide patient survey data (HCAHPS), I showed that the patient experience has a significant impact on patient advocacy. Patients who received a positive experience with their inpatient care also reported higher levels of patient advocacy toward the hospital proving that care.
The results of this analysis was conducted using the hospital as the unit of analysis, suggesting that the findings apply to hospitals in general. The results, however, will likely differ across different hospitals (See example of wireless service provider industry differences across providers). Individual hospitals can use the methodology presented here (driver analysis, correlational analysis) at the patient level of analysis to understand patient experience improvements for their hospital’s specific needs.