According to Forrester Research’s customer experience index, even improving patient experience by one or two points can have an enormous impact on patient satisfaction and a health care organization’s bottom line. It’s the proverbial win-win. However, the same report indicates that not one company across any industry is earning marks of excellence for customer experience—not even Disney, the customer-first, customer experience poster child.
Health care organizations have been, and continue to be, invested heavily in patient experience to improve outcomes and lower costs. The potential to realize these improvements depends on the quantity, quality, and diversity of data available. The post-EMR implementation landscape and health app explosion are cranking out more data than clinicians or marketers can disseminate. Interoperability is a key and complex problem health care faces, but there are means of getting answers to patient experience questions quickly without wading through siloes of data and internal bureaucracy. The key is to get to actionable insights regardless of whether your data is your own or combined with third party data from public sources or private partners.
Outcome: Actionable Insights vs. Data Quantity
Your data is not the starting point. The desired outcome is the starting point. Are you trying to acquire new members or patients? Or are you trying to reduce hospital-acquired infections in your hospital? Then find the data to help you answer your questions around this outcome. Internal data is typically hindered to varying degrees by quality and access issues that can slow progress toward your outcome.
Once you understand your internal data, search for external sources of data that can help color in the gaps of your own data. Consider the rapidly growing personal data coming from wearable devices, such as a Fitbit or Apple Watch. Or look to consumer-generated data from social media and the volumes of data it creates to support answering your patient experience questions.
Two Ways to Use Data: Flu Shot Example
Let’s look at how you can use flu shot data from both a consumer and patient experience perspective. Flu season is largely predictable and the ability to get a flu shot is reasonably accessible to the American population. Adding hyper-local weather data to a predictive model for clinic staffing can significantly reduce wait times and overtime costs. Wait times are a well-known pain point for patients so the organization gets points for an improved customer service while also being more agile on scheduling to control labor costs. These types of data projects can be relatively inexpensive to pilot to determine efficacy and value and help build the business case to more fully fund the data-driven project.
If you want to reach a patient as a consumer, this hyper-local weather data can be combined with web search data showing people that searched flu season and flu shot terms to serve-up timely, highly-targeted, highly-relevant, and in context campaigns with compelling reasons to get a flu shot. In essence, providing added value by being a health care barometer for potential health threats to your membership or local population with fewer dollars than a spray and pray campaign.
A few things to keep in mind as you approach any data project in your health care organization:
- New knowledge from big data analytics has to be practically applied. Beyond the quants and the geeks in analytics and IT, successful teams must also include clinicians, marketing strategists, and even patients to ensure that newly created knowledge is truly valuable and able to quickly be put into practice.
- Finding balance in data analytics is key. The typical enterprise IT delivery cycle for data projects is glacial, and the implications of that prolonged time-to-value and inability to nimbly respond to market changes and opportunities can hold marketers hostage in status quo far too long. On the other hand, you want to tap into your enterprise data and not totally skirt them with outside vendors to make sure you get answers relevant to your specific population and audience. Assessing your internal availability and quality is a key first step. Then look to outside data sources to augment your insights.
- Beware the cloud argument. Your IT partner and/or your outside analytics partners may list the merits of cloud-enabled analytics that make them the nimblest of the nimble, and the cloud can certainly shorten the time-to-value, but beware, this route can also be way too slow.
Insights-as-a-Service Can Address Challenges
Many analytics vendors are starting to tout “insights-as-a-service”, but this often means analytics-as-a-service that pool data into ‘insights’ that may or may not be relevant to your outcomes. True insights-as-a-service requires a predictive analytics partner with bona fide AI capabilities and pre-licensed access to third-party data as well as public data already integrated into their platform. Many vendors will go out and acquire this on your behalf, but look for vendors that bring this feature to the table for your best chance of rapid success. This type of partner can pilot a custom insights project to answer your outcomes-driven questions of the data in a 4-8 week cycle versus 4-8 months—or worse, 4-8 days, which means they don’t really have the data or possibly the data scientists to really evaluate your most pressing questions properly.
Bottom Line: Use the Right Data
Insights-as-a-service as outlined here isn’t the only way forward, but it is a method to combine the usable internal data with relevant external third-party data to better predict the behaviors of your patients or potential patients/members so that you can meet them where they are with relevant, timely messages in the context in which they are seeking information. Being armed with the right data means added value, and having more perceived value ultimately elevates your overall customer experience.
By Cindy Jennings | Posted May 8, 2018