Real Data, Real Time, Real Results

Transcription

1 Real Data, Real Time, Real Results Angela McCann, RN, MPH Quality Improvement Specialist Northeast Health Care Quality Foundation Adverse Drug Events / Readmissions Workshop March 15, 2013 Concord, NH Northeast Health Care Quality Foundation The QIO for Maine, New Hampshire and Vermont

2 This material was prepared by Northeast Health Care Quality Foundation (NHCQF), the Medicare Quality Improvement Organization (QIO) for Maine, New Hampshire and Vermont, under contract with the Centers for Medicare & Medicaid Services (CMS), an agency of the U.S. Department of Health and Human Services. The contents presented do not necessarily reflect CMS policy. xxxx-xxx-xx-mnv

3 NHCQF 10 th SOW: ADE Reduce adverse drug events and potential adverse drug events in specified population of focus (PoF) PoF Diabetics taking oral hypoglycemics Patients on warfarin Patients on antipsychotics Patients on 8 or more medications Patients with 2 or more providers Patients with 5 or more chronic conditions

4 10 th SOW Early Challenges Defining an adverse drug event Defining a potential adverse drug event

5 Evidence for Defining ADE and pade Beers List Avoid strategy START/STOPP criteria Avoid and Initiate strategy USC Tracking Tool Comprhensive tracking

6 Medication Related Problems

7 ADE and pade Classification Scale Potential Adverse Drug Event A. No med error/event, but potential for ADE identified B. Med error/event occurred but did not reach the patient C. Med error/event reached the patient, but no harm done D. Med error/event reached the patient and monitoring or intervention required to confirm no harm

8 ADE and pade Classification Scale Adverse Drug Event E. Temporary harm, requiring intervention F. Temporary harm requiring hospitalization G. Permanent harm/disability H. Life-threatening harm I. Resulted in death

9 ADE and pade Classification Scale pade Severity Rating I. Potential for minimal harm, requiring patient self-management, or no harm II. Potential for moderate harm, requiring healthcare professional intervention or hospitalization to resolve III. Potential for severe harm (permanent disability or death)

10 MRP Interventions/Recommendations D/C drug Start drug Change drug/dose/route, etc. Order labs or other diagnostic tests Educate patient Referral Clarify Rx Schedule appt with provider Rx compliance box

11 Challenges in Data Collection Delays Accuracy Cumbersome Who will do it? Consistency Organizing it Analyzing it Doing something with it Data paralysis Analysis paralysis

12 What Do You See in Your Data? Data must be easily and quickly understandable in order to become information. Data is most commonly in table format Information is gathered by moving that data from a tabular format into a visual and actionable format, such as a column chart or pie chart Visual displays must be able to be interpreted rapidly by all viewers

13 A Few Real Examples 1. Coumadin pade: Out-of-range INR in patients taking warfarin ADE: avoidable events related to supra- or sub-therapeutic INR (bleeds and clots) Flowsheet Data elements Date of INR INR value Warfarin dose Date of next INR Target INR range Indication

14 Tabular Dataset

15 Tabular Dataset

16 /1/ /8/ /15/ /22/ /29/ /5/ /12/ /19/ /26/ /3/ /10/ /17/ /24/ /31/2012 1/7/2013 1/14/2013 1/21/2013 1/28/2013 INR value INR Values /1/ /8/ /15/ /22/ /29/ /5/ /12/ /19/ /26/ /3/ /10/ /17/ /24/ /31/2012 1/7/2013 1/14/2013 1/21/2013 1/28/2013 mg Warfarin Dose Visual Display

17 What Do I Get From the Data? Total # of INR values 23 In Range 10 43% Out of Range 13 57% Supra-therapeutic 3 23% Sub-therapeutic 10 77%

18 INR value mg Another Example INR Values Warfarin Dose Overall In Range: 58% After 2 months: 67% After 3 months: 69%

19 The Data and Information Balance Data collection burden Accuracy and timeliness

20 The Case for Real-Time Data Collection Nursing homes participating in the NHCQF Medication Safety Collaborative were asked to collect real-time data elements: Total census Number of residents on antipsychotics Number of residents with schizophrenia, Huntington s Disease or Tourette s Syndrome Number of adverse drug events related to antipsychotic use (change in mental status, EPS, hospitalization/ed visit related to medication)

21 The Case for Real-Time Data Collection This data collection was too burdensome to do monthly Most, if not all, homes were not formally tracking ADEs So NHCQF pulled CASPER/MDS data for each home every month

22 The Case for Real-Time Data Collection MDS 3.0 Measure: Psychoactive Medication Use in the Absence of Psychotic or Related Condition What is a psychoactive medication? Antipsychotic What is a psychotic or related condition? How are residents identified? Numerator: LTC residents with an MDS assessment who received an antipsychotic Denominator: All LTC residents with an MDS assessment Exclusions: Schizophrenia Tourette s Syndrome Huntington s Disease

23 The Case for Real-Time Data Nursing Home X Collection Baseline rate: 28% in August 2012 Target: 50% reduction by December 2012 Interventions: Provider and staff education Collaboration with pharmacy No prn antipsychotic orders Orders given after 5pm and on weekends were onetime orders and required next day (or Monday morning) review by DON

24 MDS Measure: Psychoactive Medication Use in the Absence of Psychotic or Related Condition 35% Antipsychotic Use Rate MDS 30% 25% 20% 15% 10% 5% 0%

25 Real Time Dataset Month ending Total number of residents on antipsychotics Total number of residents % of residents on antipsychotic Total Number of Residents Without Schizophrenia, Tourette's or Huntington's Disease on Antipsychotics Percentage of Residents Without Schizophrenia, Tourette's or Huntington's Disease on Antipsychotics Residents not on antipsychotic (calculated) 6/30/ % % 56 Relative reduction from baseline 7/31/ % % 57 1% 8/31/ % % 57 13% 9/30/ % % 56 17% 10/31/ % % 66 39% 11/30/ % % 67 49% 12/31/ % % 63 41% 1/31/ % 8 9.8% 74 61% 2/28/ % 7 9.3% 68 63%

26 Real Time Data 30.0% 25.0% Percent on antipsychotics 20.0% 15.0% 10.0% 63% relative reduction 5.0% 0.0% 6/1/2012 7/1/2012 8/1/2012 9/1/ /1/ /1/ /1/2012 1/1/2013 2/1/2013 3/1/2013 Month ending

27 Another look at the same data Control Chart I-Chart Percentage of Residents on Antipsychotics 30% 25% UCL= % 15% CEN= % LCL= % 0% 6/30/2012 7/31/2012 8/31/2012 9/30/ /31/ /30/ /31/2012 1/31/2013 2/28/2013

28 Another look Number of residents on antipsychotics (excludes residents with schizophrenia, Huntington's or Tourette's) Not on antipsychotic On antipsychotic number of residents /30/2012 7/31/2012 8/31/2012 9/30/ /31/ /30/ /31/2012 1/31/2013 2/28/2013

29 How well does our external data reflect the real-time truth? Antipsychotic Use Rate MDS Real-time 35% 30% 25% 20% 15% 10% 6% 5% 0%

30 How well does our external data reflect the real-time truth? Antipsychotic Use Rate MDS Real-time 35% 30% 25% 20% 49% 15% 10% MDS Data Real-time data 63% 6% 5% 0% Relative reduction rate 49% 63%

31 TURNING DATA INTO INFORMATION TURNING INFORMATION INTO ACTION

32 Improvement Task Increase pneumococcal vaccination rate in adult population Setting: Primary care office Baseline: 35% Target: 50% Data source: direct pull from EHR

33 Monitoring Progress Pneumococcal Vaccination Rate Actual Rate 100% 90% 80% 70% Vaccination rate 60% 50% 40% 34% 33% 33% 33% 35% 37% 38% 39% 39% 30% 20% 10% 0% Date

34 Motivation to Improve: Comparison Data Pneumococcal Vaccination Rate Actual Rate Best performing QIO practice 100% 90% 80% 70% Vaccination rate 60% 50% 40% 34% 33% 33% 33% 35% 37% 38% 39% 39% 30% 20% 10% 0% Date

35 Interventions so far Workflow analysis Workflow redesign EHR templates created and revised Quarterly data fed back to practice in line chart Quarterly meetings with whole team

36 The Ah ha Moment A line graph meant nothing to the practice A rate of 35% repeated over 18 months was not motivational..only defeating A rate of 35% didn t mean anything tangible to anyone

37 The Ah ha Moment The Number in Need calculator Enter a desired rate and get a number of vaccinations required to achieve that rate To get from 35% to 40% 27 vaccinations To get to the target 50% 60 vaccinations SOP written and initiated Medical assistant could give vaccination via standing order Clinical staff ran monthly query from EHR to find patients that needed vaccine and brought them in for nurse visit Set the standard that certain prevention measures would be reviewed during intake process

38 Making Data Real Pneumococcal Vaccination Rate 100% 90% 80% 70% 62% Vaccination rate 60% 50% 40% 34% 33% 33% 33% 35% 37% 38% 39% 39% 41% 50% 114 pneumovax injections 30% 20% 10% Number in Need Calculator 14 pneumovax 0% Date

39 High Performer Achieves Higher Performance Pneumococcal Vaccination Rate Actual Rate Practice Target Rate Best performing NH QIO practice 100% 90% 85% 85% 86% 80% 70% 76% 74% 74% 77% 78% 81% 82% Vaccination rate 60% 50% 40% 30% 20% Number in Need Calculator 10% 0% 8/1/2008 3/31/2009 9/20/ /31/2009 4/2/2010 5/31/2010 8/31/ /31/2010 2/28/2011 5/31/2011 Date

40 Opportunities for ADE Work QIO Technical Assistance HRSA PSPC Collaborative For more information on either opportunity, please contact: Angela McCann, RN, MPH

41 Comments/Discussion/Questions?