Have Data, Will Cure

Data analytics for patient satisfaction and efficiency in healthcare

Published: Tuesday, June 16, 2015 - 11:42

The dynamics of the healthcare environment are changing rapidly. Small primary care practices are confronting a host of regulatory, technological, and practice challenges, not the least of which is patient expectation and evaluation. The challenges are all exaggerated by social media, where patients are quick to share their experiences.

Some of the common complaints heard repeatedly include:
• Why do they make an appointment for 8:30 and see me at 9:30? Why even make an appointment?
• Can’t the doctor spend more than 5 minutes with me?
• Why do I have to fill out all these forms every time I see a new doctor?

There are hundreds of answers for these questions, but fortunately technology lends itself to analytics to better understand scheduling and service nuances. Perhaps most important, technology can help provide solutions that will enable doctors to do the thing they love the most—practice medicine. However, this requires a change in mindsets and initiative on the part of physician offices.

This is just one example of how data analytics can help alleviate a common complaint in healthcare service, and it makes quality professionals wonder why the industry is so slow to take advantage of analytics for quality improvement. This is especially so given the fact that of the 22 top-level applicants to the 2014 Malcolm Baldrige National Quality Award, 12 were healthcare organizations. Yet, most healthcare providers seem oblivious to the fact that they have enough data to help them streamline their processes and increase patient satisfaction.

As part of any change, collecting and analyzing data is essential to improvement because what isn’t measured can’t be improved. Data, such as internal tracking of wait times, appointments types, and patient-facing time, can provide tremendous insight for making the whole operation better for all stakeholders, whether they are patients, staff, or physicians. The benefits can go far beyond direct, observable patient satisfaction. For instance, if practitioners integrate clinically with other groups and share the data they generate with payers, they can  bargain collectively with payers for higher fees. However, they do need to collect data and act on them. And although there are many software packages available to physician practitioners today, none seemed to address these patient dissatisfiers.

This issue isn’t limited to individual healthcare providers. Caring for the elderly, especially in the western hemisphere, is getting increasingly costly, primarily because they constitute a growing percentage of the total population. Many of these costs could be eliminated or reduced if the elderly could continue living in their own homes vs. being transferred to care facilities. In addition, the elderly could experience increased quality of their remaining lives.

As practiced in some countries (e.g., Sweden and New Zealand), elder care could be performed through home visits that provide a range of services such as cleaning, washing, medical assistance, and social support. Providing elder care is labor intensive, and planning for home-care activities presents many complexities: scheduling visits, routing staff, matching visit requirements and staff competencies, staff level load, and aligning quality of services. In addition, there are daily variables and last-minute changes such as staff members calling in sick, and cancelled or added visits. The process can therefore be difficult and time consuming.

Given the amount of data available these days, using analytics in conjunction with smart phone apps could help lower costs and improve quality and efficiency in these planning activities. Firms providing these services could also use an analysis-based, division-support system to create daily operative plans. Given the ubiquity of GPS tools, planning could consider geographic information, travel and scheduling logistics, and optimize activities based on heuristic algorithms. These are all being used today in various industries. Why not use them for delivering healthcare services? Benchmarking with other industries shouldn’t be a stretch. Queuing theory and dynamic route guidance are fairly commonplace and lend themselves to the healthcare service industry.

An analytics-based, decision support system would provide many benefits, not only for individual physician practitioners, but also for the larger networks:
• Reduced planning time
• Increased patient face-time; staff could spend more time calling on patients rather than sitting in meetings
• More efficient use of resources (e.g., less need to hire extra staff)
• Increase in quality aspects (e.g., continuity—keeping the number of different staff members who visit each patient as low as possible)
• A balanced staff workload
• Reduction of missed and rescheduled visits

In addition, such a system would provide managers with a better basis for economic reporting and control.

Interestingly, Sweden developed a system that uses data modeling to eliminate the manual planning of home-care unit assignments. Swedish municipalities use this system to plan staff scheduling and routing for more than 4,000 home care workers. The system increased operational efficiency by 10–15 percent; this corresponds to an annual savings of 20–30 million euros.

Regardless of the type of data, analytics should be used to improve patient satisfaction, quality of care, and efficiency in healthcare.

About The Author

Akhilesh Gulati

Akhilesh Gulati has 25 years of experience in operational excellence, process redesign, lean, Six Sigma, strategic planning, and TRIZ (structured innovation) training and consulting in a variety of industries. Gulati is the principal consultant at PIVOT Management Consultants and CEO of the analytics firm Pivot Adapt Inc. in Southern California. He holds a master’s degree from the University of Michigan-Ann Arbor, an MBA from UCLA, and is a Six Sigma Master Black Belt and a Balanced Scorecard Professional.