Clinical Data and Affect Change

Transcription

1 Visualize Granularities of Clinical Data and Affect Change Thomas Carlough, PharmD Pharmacy Services Data Coordinator

2 Oh the things we ll talk about 1. Suffer the Past Processes Walk through the technology and process before we adopted McKesson Analytics Explorer TM 2. Celebrate the Present Solutions See the insights that our team was able to describe with better visualizations 3. Explore the Future Possibilities Look at what we are planning for the future of this type of data visualizing

3 Atlantic Health System Northern New Jersey - 4 Medical Centers - Morristown Medical Center - Overlook Medical Center - Newton Medical Center - Chilton Medical Center

4 Should you listen to me? //aboutme Background in Computers Programming since 1995 Background in Healthcare Working for hospitals since 2004 Background in Informatics 4 years Healthcare Informatics experience Professor of Medical Informatics with Farleigh Dickenson University in NJ

5 Other More Tangible Objectives Understand the value of analytics and data visualization for clinical services Discover how data visualization can allow you to more quickly identify process issues Realize the power of combining multiple data sets into a single analysis to achieve greater altitude over a complex situation

6 So Where do We Start? Larisa Koshkina: image.php?image=57458&picture=roadpublicdomainpictures net/view image php?image=57458&picture=road

7 The Joint Commission (TJC) Center for Transforming Healthcare (CTH): Safe and Effective Insulin Use Aim to solve critical safety and quality problems in healthcare Solutions are created using systematic approach to process breakdown (Six Sigma) 9 th project by the CTH of which Morristown Medical Center (Atlantic Health System) is 1 of 5 participants What was the big question this project was trying to answer?

8 Why, Oh Why? This is important: > 1 in 5 patients have diabetes (Common) Insulin errors can be life threatening (Dangerous) Why, when surrounded by healthcare professionals, do patients go hyper and hypoglycemic? Source: Joint Commission Center for Transforming Healthcare CTH Insulin Fact Sheet :: Retrieved 6/26/2015

9 Patients in Defect Status? It sounds cold; but its just Six Sigma Any Blood Glucose > 180 < 70 Paul Brennan : image.php?image=89051&picture=patient sign at hospital

10 Data We Have, Data We Need We have: Laboratory Data: BG Result, Date/Time We need to identify only: Inpatients Those on Insulin Adults Only

11 Data Issues Lab data and PIS data, but cannot be directly combined Lab data has BG results PIS has patient information and medication orders (Inpatient/Adult/Insulin) No way to use SQL to query both at once since data access was not universal How can we do a crosswalk?

12 Excel! Old School VLOOKUP Not a bad option if you are doing it one time Not sustainable Petr Kratochvil :: image.php?image=5406&picture=telescope

13 The Fun Part Take this list of 77 data points and then correlate on defect status

14 Celebrate the Present Now you know how it worked for version 1 Let s look at what we can do with better tools

15 Starting from the Top The most important data point was the POC glucose result Over 16 months: 520,875 results Now in the improve phase Were We Improving? Holding Steady? Were We Regressing? g

16 Defect Rates (Cross Tab) This doesn t look like much I know but the raw data lays just below the surface 234,742 blood glucose measurements

17 Defect Rates (Simple Stratification) By month

18 Mean POC by STAY It is just as easy as it sounds Pivotting the data to aggregations (instead of raw data points) More concise population metric for a patient Think of this as their Final Course Grade

19 Mean POC by STAY Pretty successful over the course of a hospitalization Time to get down from the 30,000 foot view

20 Mean POC by DAY My initial thought (remember I was a newbie to this) was tricky tricky. You can pivot with two row IDs Hospital # Lab Date

21 Mean POC by DAY Wait Look at the difference Questions abound, hypotheses?

22 Patient Trend Mean (Patient Detail) Patient Joe Smith

23 Hospital Day Mean Here s all of our patients in aggregate. The X Axis is by Hospital Day Sliding Scale Insulin?

24 Glycemic Events Hyperglycemic Events >>>(frequency)>>> Hypoglycemic Events Extreme Hyperglycemic Events >300 BG Potential clinical sequelae

25 Easily Maintained within the Data Set The use of the Calculated Column feature was utilized to produce four columns Hyperglycemic Events: If(Lab.Result>180,1,0) Hypoglycemic Events: If(Lab.Result<70,1,0) Extreme Hyperglycemic Events: If(Lab.Result>300,1,0) Severe Hypoglycemic Events: If(Lab.Result<50,1,0)

26 Easily Maintained within the Data Set The result was an integer between 0 and 1, 1 if the event existed and 0 if it did not Using this method allowed us to add a summation to our pivot for our populations

27 Time to Expand Our Reach Encounter Data Includes bed management / registration Includes diagnoses Includes prescriber detail Clinical Data Includes home med list Includes MAR Includes electronic form usage statistics Meal Data

28 Encounter Data Patient Location

29 Encounter Data Service

30 Encounter Data Diabetic Diabetic Patients Non Diabetic Patients

31 Encounter Data Prescriber

32 Physician Group

33 Deployment of a Dashboard to Nursing Management

34 Service Line Performance

35 How Do You Ask Why? Measuring Process Understanding outcomes and affecting change requires measuring the process Investigations Prandial Insulin use Basal Insulin use Basal Insulin dose changes Overuse of sliding scale

36 Finding the Why Digging into the Detail Importing raw MAR and patient medication profile data turns this analytics software into an investigation tool Matching account numbers on pivoted aggregation data allows diving in George Hodan:: image.php?image=36643&picture=games on the beach pub p ctu et/ e age p p age 3&p ctu e ga o t e beac

37 Investigation Panel for Clinicians

38 Future Process Measures Electronic form Usage (data already available and integrated) Appropriate Basal dose changes after correctional insulin use Challenge: Capture correctional dose for day N and prepare boolean (T/F) if basal dose is the same Timely administration of insulin Are we giving i our insulin timely? Timely capture of BG measurement Are our BG measurements being performed too close to meals? Are we correcting insulin with Sliding Scale too often? Percent administrations vs. POC checks

39 The Road Ahead Data Governance Finalizing definitions and populations across the health system Organizing data sources so we are all using the same information Analytics Infrastructure New hires Reorganization Culture of analytics Very exciting!

40 Success Measures Initiative Measure Outcome Cost Cost of Adverse Drug Events Not yet quantified Quality Defect Rate Reduced!! Utilization Efficiency Excess Days associated with poor glycemic control Data at everyone's fingertips when they want it Not yet quantified Priceless!

41 Questions? Gwe Carlough n Cat :: Michelle C LinkedIn: Twitter: Thomas.Carlough@AtlanticHealth.org

42 Images in this Presentation All images in this presentation are within the public domain, many are sourced through I have no affiliation with this website but appreciate public domain and creative commons artists. All graphs and visuals otherwise included were created by McKesson Performance Analytics Explorer TM

43 Thank You LinkedIn: Twitter: