Reducing Readmissions Part 4: Managing Data & Analytics to Reduce Readmissions
Data needed by business intelligence analytics to support forecasting 30-day hospital readmissions span a wide range of data types, storage and accessibility requirements. And the sheer volume of this data, the number of times the data are accessed and the type of people and nature of processes accessing this data demand careful consideration. Without which, scalability may be impacted and/or other forms of personal and financial harm to the patient and healthcare organization may arise. In this final post of a four-part series, we present insight from Michele Russell, CEO of Russell Consulting Group, on these issues and how to manage data and analytics to reduce readmissions.
In our three previous posts, healthcare thought leaders shared their insights, ideas and approaches about using business intelligence analytics to avoid unnecessary hospital readmissions. You can read them here:
- Data Analytics to Predict When a Patient Will Readmit – Andrew Satz of Metrix Labs
- A Care Transition System (CTS) to Reduce Hospital Readmissions – Ed Kirchmier of AAJ Technologies
- Dashboard to Forecast Healthcare Outcomes – Kevin Oppenheimer of KGO Consulting
General Overview on Hospital Readmissions
We’re going to start with a general overview on hospital readmissions. So, as you know, CMS put in place the Hospital Readmissions Reduction Program where CMS may withhold up to 3% of your regular reimbursements for hospitals that have a higher than expected number of readmissions within 30 days of discharge.
CMS saw that patients may be readmitted for other than the fact that they just contracted pneumonia. Yet it may have a lot of other ties to reasons why they were readmitted. For instance, when they left the hospital where did they go? Did they go back home with the loving family to take care of them? Did they go back out on the street? Or into a homeless shelter where they don’t have those common, necessary items: access to medication, access to warm clothing? Things like that.
Majority of Hospitals Face 30-Day Re-Admission Penalties
In the last couple of years when we looked at the data, we saw that about 80% of the 3,200 hospitals were faced with penalties and we were looking at those hospitals data that was assessed from 2016 and 2017. Now the penalties are going to take place in withholding of payment of over $560 million dollars in the fiscal year of 2018. So, if you look at that, we’re looking at 80% of the hospitals that were evaluated. It could pertain to your hospital today and there are several that we know of that it did pertain to and they are now facing those penalties.
Protected Health Information is Like a Dozen Eggs
So, I’d like to go a little bit into HIPAA privacy and security and risk mitigation. Rather than just go through the basics of HIPAA privacy and security which you’ll see listed here, I’d like to relate that to a different sort of story. One of my personal pet peeves, something I cannot stand to do, is grocery shopping. The thing that bothers me the most about grocery shopping is the vast number of times I have to touch the items that are going into my cart that are going to go into my house. So, if we look at our protected health information (PHI) as a dozen eggs.
You go to the grocery store. First you have to find that protected health information, that dozen eggs. That is your responsibility to care for. Next, we take that, we put it into our cart. From that point, thinking of our dozen eggs as protected health information, now we have to go to the checkout, place that dozen eggs on those belts. Have that dozen eggs scanned, touched by someone else, then bagged and touched by someone else, put back into your cart, taken to your vehicle by you or by the person bagging, then placed into your vehicle, driven back to your house, taken back out of your vehicle into your home and finally put in the refrigerator.
Data is Touched Many Times by Many Different People
Those are a lot of changes in the state of that item; which is exactly the same as the state of your data. Whether it be data that is not necessarily sensitive, whether it’s protected health information for your medical and any type of medical data or your PII, or if it’s highly sensitive information such as behavioral health care data, HIV data, etc. All of those can be a component of that carton of eggs. So, what we’re trying to do here is, because there’s so many changes in the state of your data, we need to make sure that it is protected as well as possible in every one of those data states. Now if we consider that PHI as our dozen eggs, that we have to handle it with extreme care and make sure that the right people are handling that data.
Scaling Data Access and Protection via the Cloud
Think of it now in a mass production environment, which is where you are today in the hospital world: millions and millions of eggs being handled every day. This is what you deal with on a daily basis. You’re dealing with millions and millions and millions of patient records and millions of potentially protected health records and potentially highly sensitive health records. So, keeping that in mind, let’s think about – with the HIPAA and the PHI in mind – where we really want to have that data live. There are three different areas, three major types of cloud storage we have today: we have the public cloud which would be your Amazon, your Google, your Microsoft Azure where a lot of folks are moving these days so that they can avoid having to have their own their own hardware requirements, their own refresh requirements. Again, what we’re doing today with data is evolutionary. We’ve never used data so much and had so much data to use in the past. So more and more and more data, meaning you have to have faster computer time, you have to have higher storage requirements, and you need to make sure that data is highly accessible.
Leverage Cloud Environments for Different Data Types
So, whether it be in your own data center, whether it be in a public data center like here in Miami we have Terremark; or anywhere else where you may keep that, from a private perspective when it’s public you don’t have to deal with that. So, you typically either pay by storage usage, subscribe to it by person or by user, or by your compute and data accessibility requirements. So more and more, we’re moving to the public cloud because of the scalability. But a lot of folks because they do have that highly sensitive data, that HIV/aids data, that behavioral health data, and so on and so forth, may be looking at a private cloud model. With your private cloud model, you have complete and utter control over that data. You also have the responsibility to make sure no one else gets to it, which is a large responsibility. And again, as we know today, 70% of data breaches are not caused by hardware firewalls, somebody trying to get to you from the outside, they’re getting to you from the people on the inside. So, we know all that’s happening right now. So that’s a major risk and a private side of thing but it’s still highly useful with extra sensitive data. And then we’ll look at the hybrid cloud, which is a combination of private and public model. And there you really have to look at what is it that you can afford to put out in the public cloud. And what is it that you cannot afford to put out in the public cloud.
And That’s a Wrap on Reducing Readmissions Blog Series
This post completes the recap of the four presentations from our webinar on “Reducing Readmissions with BI & Analytics.” For more information on reducing potential harm, unnecessary expense and financial penalties associated with 30-Day Hospital Readmissions, reach out to AAJ Technologies using the form below.