Big issues, big trends and big numbers characterize the healthcare industry today. Consider some of these critical issues and trends that we as a nation, and particularly in the healthcare industry, are currently facing:
•Waste in healthcare estimated to be as much as 30%. That’s $600 billion dollars in a $1.8 trillion dollar industry.
•An estimated medical error death rate between 45,000 to 98,000 annually.
•Figures indicating 45,000,000 uninsured people in the U.S. This number could easily be more.
•The percentage of people on regular prescriptions right now at 44%.
•Costs in the healthcare industry are rising by 8% per year. Compare this to just over 3% for the entire U.S.
•An Electronic Health Record (EHR) adoption rate that ranges from approximately 13% for private practices to 28% for hospitals.
•A likely doctor shortage by 2020, which is likely to exceed 200,000.
•A U.S. population age 65 and over (the largest users of healthcare services) by 2020 that will grow to over 20%.
•An enormous amount of new medical information that a physician or other clinician is inundated with each year. This is equivalent to 800 years worth of reading and study by that same person.
Big problems? Absolutely. Solvable by one person? Absolutely not. Solving or reducing any one of these issues will keep thousands of people busy for years.
Business intelligence can help organizations solve their own problems, which will reduce each of these major national problems. Justifying the big projects and investments in business intelligence requires focusing your efforts on tackling one or two major drivers, such as those described above. But no matter how big the driver or the solution is, business intelligence must be used by real people daily. Otherwise it will just be another problem for your organization to confront.
Data Must Be Used by Real People
There is another side to business intelligence success as well. This is the side that generates returns at the street level. Every day, people do extraordinary things with the data they have, while constantly looking for even more.
Identifying business intelligence’s value at this level offers two distinct advantages over aiming to solve big problems. First, the return you receive on your business intelligence investment is more stable and reliable. It is much like diversifying your portfolio. You accumulate many smaller successes, which usually offer the same payback as getting the entire return from one source. At the same time, however, you have spread the risk.
The second advantage of having many small business intelligence successes is that they feed upon each other. When people read or hear about their peers’ other success stories, they are more likely to emulate them. Both friendly competition and intense cooperation often results. Obviously, people like to believe that they can be successful too.
While there are numerous stories about business intelligence success, certain stories are extremely memorable. When accomplishing these successes, extraordinary things happened for their patients, their organizations, their professions and their careers. Most are within the healthcare industry, but some are in other industries. Either way, you should be able to find similarities to occurrences in your organization.
Ten Feet Tall.
Mindy is a young nurse working for a well-respected physician specializing in diabetes care at a clinic near my home. Her story used to be the “98-pound weakling” tale because she was young and lacked the experience of the senior nurses, as well as the medical depth and authority of the physicians. Mindy’s role is to manage the information about the diabetes population for both her physician and the entire clinic. After vigilantly examining data in the patient registry repository, she used her analytical abilities in several ways for the organization. For instance, Mindy:
•Prioritizes patient visits using various factors such as the patient’s time since last visit, the severity of the patient’s condition (indicated by clinical lab results) and notes regarding the difficulty in getting patients to take care of themselves.
•Organizes the care to be provided during visits by proactively alerting other members to be ready to provide lab tests, foot exams, etc.
•Persuades the crankier patients to get the care they need. For instance, patients sometimes underestimate the time since their last visit. Mindy has the correct data in front of her, convincing them that she is not going to be a pushover.
•Participates regularly in process and quality improvement efforts, using the data she manages. Once, in a high level meeting, she was able to quote a statistic based on real data about real patients. All heads turned toward her. Now the senior executives recognize how valuable she is. They frequently share their own wisdom and ask for her opinions on current matters.
This recognition came at a price, though. She had to work through a lot of data discrepancies and analytical process issues before getting to this point. Today, several of her analytical processes have been incorporated into the organization’s care management system. And they keep coming back for more.
As she puts it: “When I have good data, I am ten feet tall!”
A Mouse that Roars
Owen is a small, modest, tidy man who tells wonderful stories about vacationing with his wife and their grandchildren. But the story other people tell about him is of “a mouse that roars.” Owen manages data for a state agency that handles a certain type of complex claim for the federal government. Essentially, he provides information to the claims representatives so they can do their job. A few years ago he found that this same data could also be used to provide information about the claims representatives.
For example, each of the steps in the claims process, as well as the time it takes to move from step to step, could be used to manage and improve those very same processes. In effect, he developed the analytical processes and data requirements for what is now regarded as one of the “next big things” in business intelligence—the process warehouse.
Using this information, Owen and his co-workers can triage claims quickly, assign claims efficiently based on a rep’s experience level and specialty, and classify claims into low, medium and high-touch categories. All of this made their process smooth, efficient and very “customer-friendly.” My mother-in-law, who worked for 30 years on the federal side, said that this particular state agency always impressed her and her managers.
How does this mouse really roar? He is invited by his counterparts at other state agencies, the federal government and private insurance companies to write, speak, teach and guide them in order to replicate his success. In fact, he had already traveled to 38 states on his “tour” when I met him.
Stacy was the buyer for towels at a discount retail chain. Her primary goal was to increase gross margins for her line of business by one percent. On $30 million in annual towel sales, this means she would add $300,000 to the bottom line. Her strategy was segmentation, which is the process of merchandising in layers of good, better and best. The company had just implemented a data warehouse of sales, margin, purchases and inventory data. Using the reporting and analysis front-end to slice, dice, sort and drill into her data, we worked together to find her one percent. It took one week.
Not bad for a week of work, but it didn’t stop there:
•She did it again, and found another one percent.
•She found a number of other opportunities, such as unloading several thousand dollars worth of poor merchandise across the stores in the chain.
•Prior to having this data, she complained that her vendors had more information than she did. This put her at a disadvantage in negotiations. Now she had the data and knew how to use it to her advantage.
•Several of her peers became interested in her analysis methods, and tried to duplicate her success. This system had 1500 active users in various roles in the organization, so her story became a shining beacon to them.
Six nurses in a small-town clinic worked together like a well-oiled clinical machine. Despite this, they had trouble helping the physicians persuade certain patients to take the steps needed to manage chronic diseases. In a brown bag session, they decided to use statistics to persuade patients to do what is right for themselves. Using data from their patient registry, they downloaded two sets of data. The first were clinical measures for the individual patient, and the second was a comparative average measure for people in the registry who were similar to the individual. After downloading these two sets of data, these nurses created a simple line graph with two trends: one for the patient and one for the average. This line graph helped them and their physicians become more persuasive, relying on two factors plain in human nature:
•It gave the patient ownership of their numbers over time, which caused many of them to want to beat their own scores.
•It gave the patient a benchmark score to either work toward when falling short, or giving them something to feel good about after exceeding the benchmark.
In both cases, patients experienced increased ownership over their own numbers and health. This made visits easier to schedule, more likely to be kept, and more productive for everybody when they occurred.
A medical supply company had a rapidly growing morale and retention problem among its engineers. Previously, these engineers had been assigned to shifts on the phones, helping to make sales and answer service questions. This move was made to solve another problem, the lack of product knowledge among the regular staff manning the phones. The engineers took this as an insult to their educational level and their position in the company. Thus, morale, sales performance and retention rates were low.
Then the company took a brilliant step. They provided the engineers with business intelligence capabilities on product sales. Now, instead of merely pushing product, the engineers could ask insightful questions about the customer’s needs and speak with authority regarding what products were selling and why. Upselling, cross-selling and true phone consultancy all increased dramatically.
This was a two-for-one deal:
•Morale and retention increased beyond previous levels. The engineers were actually enthusiastic about their rotations on the phones.
•Sales and customer satisfaction increased because the engineers were experts, who had knowledge and wisdom to share with “their” customers. In turn, the customers appreciated it.
Implementing a program of business intelligence applications is a major investment for any organization, in terms of both money and the effort needed to change mindsets and methods. It is essential that you and your organization can justify each project within such a program. This justification, though, sometimes doesn’t come from one big bang. It can come from recognizing many smaller “pops.” When this happens, your organization will transform organically. There will be a “new normal” in your organization. And the industry as a whole will benefit.
But you must start somewhere. Search for local stories where people are already putting their data to good use, even if they are struggling to gather and manage that data. This is where business intelligence can make a real difference.
Thanks for reading. I look forward to your comments.