Can You Decrease Readmissions By 50%?

The June 10, 2014 issue of Hospitals & Health Networks (H&HN) contained the article Technology is the key to patient engagement at the individual level. It is worth reading. http://ow.ly/ylgxZ

It got me wondering about how we define engagement, wondering about engaging patients, and about engaged patients. I think engaged patients are the result of different efforts. Most efforts to engage patients stem from efforts made by the hospital.  They tend to be one-way, from the hospital to the patient. They reflect how the hospital feels its patients need to be engaged.  What they miss by not being two-way is a knowledge of how patients feel they need to be engaged with the hospital.

If the engagement were two-way both the patients and the hospitals would benefit.

I believe technology will be key to patient engagement. I think that designed correctly technology should play a major role in reducing readmissions. I also believe that someone should consider asking the patients how technology could help them.

I recently developed a patient access/experience strategy for the call center of a large teaching hospital. One finding was that 99% of all of the patients who asked to speak with a nurse received a voice mail stating that a nurse would get back to them within 48 hours. Because of my fear of large numbers I did not calculate the cost of those callers who went to ED, but it was orders of magnitude higher than the cost of having a nurse or two in the call center. Most of those who went to the ED did not have an emergency. Many simply wanted a refill.

Let’s look for a moment from thirty-thousand feet at how the discharge process works at most hospitals. When I am discharged I sign my discharge orders, and if I am lucky someone from the hospital calls me in a few days to ask how I am doing or feeling. If someone calls me on day three, and my wound opens on day four, or I am feeling sick, or there is a complication from my treatment or from my procedure or from my medication or from something new, what are my likely responses?

I could call the hospital—see above; I could ignore it; or I could go to ED.

If I was unsuccessful previously calling the hospital, I may not even consider that option. If I call, I might speak with someone who could help me, or I could get a busy signal, I could be put on hold, my call could be transferred, or I could be sent to voice mail—see above. Four of those responses are not good for me, and all five may not be good for the hospital.

Why? If I do not get to speak with someone, chances are that I will solve my problem by going to ED. If I do speak with someone they may tell me to go to ED or to the hospital. Chances are good that the hospital is going to incur a cost and record a visit that may
not have been necessary if the hospital had provided me with a technological
alternative.

What might that technology look like?

I see it working something like this.

Before I am discharged the hospital adds me to their discharged patient portal, an interactive portal that contains information about the specifics of my illness or procedure—my meds, their side-effects, complications that could occur and what I should do about them, symptoms that may arise and what I should do about them. The portal also allows me to input data. I can input that I took my medications and any side-effects I am having. I can input any complications, my diet, exercise, BP and pulse, weight, and any
questions I may have.

The system would be designed to alert someone at the hospital each time any of the data I input is outside of the acceptable norms. This way, instead of me playing doctor and determining what I should do, the hospital can act before I act. They can have someone call me, can send a nurse to my home, or can send a physician to my home.

Not every patient will use this technology, but each one who does will not only be doing themselves and the hospital a favor, they will be more engaged and will have a better overall experience.

 

The Fish Doctor’s Fallacy on Improving Population Health Management

Pretend for a moment that you are in ichthyologist, a fish doctor. And your job is to manage the health of tens of thousands of fish in a very large pond, the same job that your colleague had last year.  To accomplish your task, each week you come to the pond Monday through Friday—ichthyologists are in a union and they do not work weekends. And each day you capture and evaluate the health of one hundred fish at random, examining the same fish each day that week.  You find a variety of fish ailments among the fish you examine, and you treat each fish according to its needs.  Over the course of a year you may examine and treat some of the same fish more than once.

Over fifty weeks—you get a two week vacation—you have examined and treated five thousand fish.

Let us examine the question of whether or not your approach to managing the health of the fish in the pond worked.  How can one determine how well have you done your job? If there was a scale to manage the effectiveness of your approach, at one extreme would be that examine the health of all of the fish at the end of the year and record that on average the fish were healthier than they were a year ago. At the other extreme, you would come back at the end of the year and find that all of the fish were doing the backstroke.

Your approach relies on that belief that examining and treating a given fish over a single weeks’ time will give you the information you need to ensure that that fish will be healthy throughout the year.

Your approach also relies on the belief that examining and treating only five thousand of the tens of thousands of fish over the course of a year will give you the information you need to ensure that the average level of health of the fish in the pond will be better than it was last year.

If it sounds simple, that is because it is—too simple.  Too simple to be effective.

I used to be a mathematician; I know that is difficult to believe.  I have forgotten most of what I learned, but I retained just enough to be a boorish hit at parties.  There is something called the Law of Large Numbers. It is used in probability theory. In principle, it describes the result of performing the same experiment a large number of times. In theory, the average of the results should be close to the expected value.  The more trials you perform, the closer you should expect to be to the expected value. Using a large number of trials should result in stable long-term results for the average of these random events.

The Law of Large Numbers has value in the population involved in your experience is too large to run the experiment on the entire population.

As an example of an experiment, think about predicting whether the flip of a coin will result in a head or a tail.  The probability of tossing either a head or a tail is ½.  The probability of tossing five heads in a row is 1/32. There is something called the Gambler’s Fallacy which works as follows.  Most people, who saw the coin come up heads five times in a row would bet that the next toss of the coin would be tails.  Most people would be wrong since there is still a fifty-fifty chance that the next toss will be either a head or a tail.

The Law of Large Numbers also relies on the fact that the trials, the sample data, will asymptotically—I can’t believe I spelled that correctly—approach the expected result.

The converse to the Law of Large Numbers is the Law of Small Numbers, also known as a Hasty Generalization, and the Pigeonhole Principle. Hasty generalization’s fatal flaw is that it relies much more heavily on the belief of the expected outcome than it does on the sample size of the experiment of the population being investigated.  The false belief that was created before the process began that the trials will yield the expected outcome adds a bias that invalidates the approach.

Someone asked me why I think Patient Access/Customer Experience (PACE) plays a vital role in the success or failure of Population Health Management (PHM).

I have spoken with several hospital executives about their efforts to effectively implement a program of PHM.  Some of their names would be familiar to you.  This is what I learned from them about what they are doing.

They believe that the success of their efforts is tied to the amount and quality of the data they can collect on the people who visit the hospital, patients.  Some hospitals even collect data a few days before the person comes to the hospital and for a few days after the person leaves the hospital.

They believe the data do two things for them; manage the health of a given patient over time, and use that person’s data, in conjunction with similar data from other people with similar health problems to foretell the needs and manage the health of that group of people over time.  Lastly, the information from various patient groups could then used to glean the needs and improve the health of the population as a whole.

Ichthyology and Hasty generalization.

  1. Can my health be managed based only on data collected when I am in the hospital?
  2. Is there any data to manage my health if I do not come to the hospital?
  3. Can this approach be effective for managing the health of an entire population?

There is a solution to the problem of the Law of Small Numbers, and fortunately the solution does not require having the entire population at the hospital every day of the year.

What is the alternative to having the success of PHM rely solely on having the hospital capture data on everyone every day of the year?  Why not have the hospital manage data, and draw inference from data that the members of its population input? Why not create an interactive (2-way) vehicle that allows:

  • People to input data about their health:

o   Diet

o   Exercise

o   Adherence to medications

o   Weight

o   Pulse and blood pressure

o   Requests to speak with a nurse or doctor

o   Requests refills

  • Hospitals to monitor the health of an individual:

o   Correlate that data with similar individuals

o   Contact an individual when a person’s data is outside of expected boundaries

o   Send a physician or nurse to the person’s home

Under this type of a Patient Access/Customer Experience (PACE) tool hospitals are no longer limited to only collecting data for people only when they are in the hospital. Using this type of tool hospitals have more data about an individual, and have more data on more individuals.

This same tool can be used to decrease readmissions.  People want to be well, and allowing them to play an active part in communicating their health is a win for both parties.