Please complete all of the following sections. Submission is limited to a maximum word count of 1500 (not including text in graphs). Overview: Describe 1) where the work was completed (in what type of department/unit); 2) the reason the change was needed; 3) what faculty/staff/patient groups were involved, and 4) the alignment to organizational goals. Underuse of evidence-based care for chronic disease management and prevention remains a major quality problem. Standard build electronic health records(EHRs) often lack intelligent decision support to measure and improve quality. UT Southwestern Medical Center in Dallas (UTSW) has used the outpatient Epic EHR for 8 years, but was unable to harness EHR data for QI. The generic build of Epic is infamous for being an IT ‘roach motel—data goes in, but it doesn’t come out.’ We undertook this UT Health System-funded IT QI grant at UTSW as a multidisciplinary collaboration between all 4 adult primary care practices in General Internal Medicine, Geriatrics and Family Medicine, and our IT, Quality and Patient Safety, Health Services Research, and Medical Informatics units. Medical informatics experts at Northwestern Univ. with 6 years of experience optimizing outpatient Epic, were also part of the project team. These 4 practices and 66 physicians care for 28,000 individuals and manually-obtained quality reports suggested we lagged many national quality benchmarks. The project aligned with institutional goals to: 1) obtain NCQA Patient-Centered Medicine Home accreditation, 2) benefit from pay for performance(P4P) quality incentive programs, 3) establish an outpatient quality measurement program, and 4) gain local IT expertise in customizing Epic to improve quality, efficiency, implement intelligent decision support, and meet meaningful use criteria. Aim Statement (max points 150): Describe the problem that you sought to address. Our aim was to customize the EHR to: 1) Create a provider-friendly, exception reporting system whereby clinicians can efficiently document medical or patient reasons why quality measures may not apply to a given patient to improve the accuracy of quality indicators; 2) Implement new, customized Best Practice Alerts(BPAs) in Epic to serve as a user-friendly, one-stop-shopping, low-hassle functionality that identify any quality deficits in real-time so a provider can act on them during patient visit as part of routine
PROJECT NAME: Making both the EHR and Providers Smarter: Optimizing the Epic EHR to create accurate ambulatory quality measures and user-friendly Best Practice Alerts to drive quality improvement.
Institution: UT Southwestern
Primary Author: Ethan Halm Secondary Author: Jason Fish, Deepa Bhat Other Authors: Temple Howell-Stampley, Lynne Kirk, Brett Moran, Manjula Cherukuri, Kim Batchelor, Heather Schneider Project Category: Effectiveness
Choose most appropriate category: 1) Patient Safety, 2) Patient Centered Care, 3) Timeliness,
work flow; and 3) assess the impact of a multifactorial audit and feedback and EHR- based decision support intervention on improving our performance on national quality indicators. We focused on 19 high priority quality measures across chronic diseases (diabetes, heart disease) and preventative services(cancer/osteoporosis screening, immunization) showing suboptimal performance. We implemented this across all 4 adult primary care practices staffed by 66 physicians. Measures of Success: How did you measure the impact of your proposed change? Our primary outcome focuses on the 19 high priority chronic disease and prevention measures, specifically whether the quality measure was satisfied by completion of the recommended service or therapy or an appropriate exception was documented using the BPA. We tested for intervention effects by fitting logistic regression models of measure completion with a fixed, 2-level time factor(pre- and post-intervention) and random effects to account for repeated measures within patients. We interpreted statistically significant improvements(P<0.05) over time as evidence of an intervention effect. All tests were performed using SAS software. As secondary outcomes, we are examining how often the different components of the Epic enhancements(exception documentation, BPAs) were triggered, used, ignored, or partly used as a way of assessing adoption, familiarity and engagement with the EHR. Use of Quality Tools (max points 250): What quality tools did you use to identify and monitor progress and solve the problem? Provide sample QI tools, such as fishbone diagram or process map. We have used several complementary QI tools. One was the creation of standardized, normed audit and feedback quality reports. These reports include provider-specific numeric performance, clinic averages, and NCQA HEDIS targets, as well as visual gestalt comparisons to national benchmarks(green-yellow-red visual performance; See Attachment 1). In addition, we have practice-level pre- and post-BPA implementation reports used to refine the BPAs and practice workflow(Attachment 2). During the planning phase, the multidisciplinary team met several times brainstorming on strategies for evaluating the current state, development of the BPAs, and process improvement strategies. Examples of Fishbone diagrams, Pareto charts (Attachment 3), and Process Maps used to assess reasons for missing labs in patients with diabetes are attached (Attachment 3). In the implementation phase, Decision Tree analysis (Attachment 4) was done to understand exception reporting trends and improve provider engagement. We also used PDSA cycling to maintain and refine the BPA programming logic. Interventions (max points 150 includes points for innovation): What was your overall improvement plan? How did you implement the proposed change? Who was involved in implementing the change? How did you communicate the change to all key
stakeholders? What was the timeline for the change? Describe any features you feel were especially innovative. The overall improvement plan was to accurately measure, report and improve performance on 19 chronic disease and preventive service national quality measure. We developed, refined, programmed and implemented BPAs for 19 measures over a 12 month period. Prior to and upon implementation, our providers were trained on the rationale for and use of the BPAs through a series of in-person provider meetings, as well as email dissemination of a 4 minute voice over text narrated video demonstrating proper BPA use using animated Epic screen shot movies. Several innovative IT elements are worth noting. First, we developed and refined computerized algorithms for case identification(getting the ‘denominator’ of the quality measure right) that pulled ICD-9 data from the: encounter, problem list, medical/surgical history, and health care maintenance modules. This approach was more sensitive at identifying eligible patients than relying on one element(e.g. problem list). Second, to get the ‘numerator’ right, we did two things. We pulled lab, procedure, and imaging results straight from the EHR. We also used data from the new ‘exception reporting’ buttons we created for all BPAs since clinicians often know things about patients not readily ascertainable from structured electronic data. We created novel exception buttons for the following scenarios: 1) patient did not have the disease(false positive, past diagnosis of gestational diabetes now resolved); 2) medical contraindication to recommended care(symptomatic bradycardia on beta-blockers in MI patient); 3) patient reason something wasn’t done(patient refusing vaccination); or 4) test/procedure completed outside of UTSW(colonoscopy done by community gastroenterologist). The system saved these data so physicians did not keep getting ‘penalized’ for patients for whom the measure should not apply or was satisfied by data the doctor, but not the EHR, knew about. Third, BPAs were structured as ‘passive’ alerts not ‘hard stops’ that interrupt clinical work flow. This was designed to prevent ‘pop-up fatigue.’ A single BPA tab on the left of the screen was highlighted yellow if something was due. The provider could click on that tab to see what was due if/when they wanted. When they clicked on this, it made it easy to do the right thing(displayed last values, relevant data, and SmartSet to satisfy the measure with one mouseclick). Results (max points 250): Include all results, using control charts, graphs or tables as appropriate. From an outcomes perspective, on the 19 quality indicators, we saw statistically significant improvements in 13(p<.05) and a trend towards improvement in another (p=.06; See 3 results tables in Attachment 2 for details). This included significant improvements in all 5 preventive services with absolute improvements from 4% to 14.6%. Improvements were also seen for lab monitoring (A1C, LDL, nephropathy) and medication use(antiplatelets, beta-blockers, ACEI/ARBs). No improvements were seen on A1C, LDL or BP control which were not explicitly targeted with improvement strategies by the BPAs. Exceeding national benchmarks increased from 8/19 measures pre-intervention to 12/19 post-intervention. From a process perspective, post-
intervention over 40,000 BPAs triggered and over 5,000 exceptions were documented. Exception reporting improved performance in several areas. For example, colorectal cancer screening was 70.7% pre-intervention, improved to 73.9% after BPA activation, and further increased to 84% factoring in exception data(p<.05). Revenue Enhancement /Cost Avoidance / Generalizability (max points 200): What is the revenue enhancement /cost avoidance and/or savings for your project? Did you implement this project in multiple sites after determining that your change was successful? This project has had several implications for revenue enhancement. First, it enabled us to obtain Level 3 NCQA Patient-Centered Medical Home accreditation, and we are now negotiating shared-saving contracts with two commercial insurers for the upcoming year based on this. Second, it led to recognition in the national Bridges to Excellence in diabetes program which yielded a $15,000 bonus to the practice, and recognition in Blue Cross/Blue Shield of Texas’ Diabetes P4P program that will yield an additional quality bonus of $100 per patient meeting benchmark/year (estimated to yield $25,000- 50,000/year). We are in the process of applying for the sister, national Bridges to Excellence P4P program in heart disease that ought to yield similar financial benefits. The institution is also planning on disseminating the diabetes and heart disease BPAs to the Endocrinology and Cardiology clinics, and applying for Bridges to Excellence P4P programs in these clinics. These accomplishments should also yield quality bonuses from the CMS PQRI program. The institution is also planning on rolling out these IT- enhancements to other primary care practices that UTSW is looking to bring into an affiliated community practice network that would use our Epic EHR. Finally, this suite of ambulatory Epic enhancements and BPAs are being implemented in the 11 community- oriented primary care clinics run by Parkland Hospital and Health System, our affiliated safety net provider. Conclusions and Next Steps: Describe your conclusions drawn from this project and any recommendations for future work. How does project align with organizational goals? Describe, as applicable, how you plan to move ahead with this project. Many of the next steps are outlined above with regard to dissemination to other UTSW and affiliated clinics. We also plan on continuing to further refine the visit-based electronic case identification algorithms and BPAs. Our next phase will be also add a population management component to the program using the practice-based quality reports to proactively reach out to patients not meeting quality indicators using our practice care managers and nurses. Additionally, the multidisciplinary project team will evaluate, prioritize, and design a second set of IT-enabled quality measures and BPA decision support tools. Institutionalization of two aspects of the project will further ensure sustainability. The Epic programming for future BPAs has now shifted to a UTSW IT employee on the meaningful use team and the data analysis has shifted to a programmer in our Office of Quality and Safety (from grant funding). Total abstract word count: 1493
E P IC P a g e 1 o f 7 D a t a S o u r c e
N a t i o n a l m e a n s r e p o r t e d b y N a t i o n a l C o m m i t t e e f o r Q u a l i t y A s s u r a n c e ( N C Q A ) H e a l t h c a r e E f f e c t i v e n e s s D a t a a n d I n f o r m a t i o n S e t ( H E D I S ) f o r t h e y e a r 2 0 0 9 / 2 0 1 0 . B P m e a s u r e m e n t n a t i o n a l a v e r a g e l i s t e d i s p r e f e r r e d p r a c t i c e s t a n d a r d .
N a t i o n a l A v e r a g e s D i a b e t e s : D i a g n o s i s c o n f i r m e d b y t h e p r e s e n c e o f I C D 9 2 5 0 . X X , 3 5 7 . 2 , 3 6 2 . 0 X , o r 3 6 6 . 4 1 i n t h e e n c o u n t e r d i a g n o s e s ( p a s t 5 y e a r s ) , p r o b l e m l i s t o r m e d i c a l h i s t o r y r e c o r d s .
A s s i g n e d P h y s i c i a n : P C P w i t h w h o m t h e p a t i e n t h a d 2 o r m o r e v i s i t s d u r i n g t h e t w o y e a r p e r i o d . I f a p a t i e n t c h a n g e d P C P s d u r i n g t h e t w o y e a r p e r i o d a n d h a s 2 o r m o r e v i s i t s w i t h m o r e t h a n o n e P C P , p a t i e n t w a s a s s i g n e d t o t h e P C P w i t h t h e m o s t v i s i t s . I f p a t i e n t w a s s e e n e q u a l n u m b e r o f t i m e s b y o n e o r m o r e P C P s , p a t i e n t w a s a s s i g n e d t o P C P a s o f l a s t v i s i t .
E s t a b l i s h e d P a t i e n t : P a t i e n t w i t h 2 o r m o r e o f f i c e v i s i t s w i t h t h e s a m e P C P i n t w o y e a r s ( J u l - 2 0 0 9 t o J u n - 2 0 1 1 ) w i t h a t l e a s t o n e v i s i t i n t h e l a s t 1 2 m o n t h s ( J u l - 2 0 1 0 t o J u n - 2 0 1 1 ) . K e y D e f i n i t i o n s A l l e s t a b l i s h e d p a t i e n t s , a g e s 1 8 - 7 5 y e a r s , w i t h a d i a g n o s i s o f d i a b e t e s . I n c l u s i o n C r i t e r i a
O f f i c e o f Q u a l i t y I m p r o v e m e n t & S a f e t y D R A F T P a t i e n t C e n t e r e d M e d i c a l H o m e C li n i c a l O u t c o m e s - D i a b e t e s M a n a g e m e n t P r e p a r e d b y : O f f i c e O f Q u a l i t y I m p r o v e m e n t & S a f e t y a s o f 2 1 M A Y 2 0 1 2
Diabetes Management EPIC Provider ID: 1234 Provider Name: Provider Last Name, First Name
Reporting Period: Jul-2010 to Jun-2011
GIM Practice Provider: 1234 NCQA
Measure Number Eligible
Met National Average
HgbA1c measured in the last 12 months 1607 88.4 126 92.1 89.3
HgbA1c < 8% (denominator: all diabetics) 1607 70.2 126 77.0 62.4
HgbA1c < 8% (denominator: all diabetics with non-missing values) 1421 79.4 116 83.6 .
LDL-C measured in the last 12 months 1607 81.7 126 88.1 85.9
LDL-C < 100mg/dl (denominator: all diabetics) 1607 55.3 126 51.6 48.2
LDL-C < 100mg/dl (denominator: all diabetics with non-missing values) 1313 67.6 111 58.6 .
BP measured in the last 12 months 1607 97.1 126 99.2 95.0
BP < 130/80mmHg (denominator: all diabetics) 1607 37.7 126 38.1 33.6
BP < 130/80mmHg (denominator: all diabetics with non-missing values) 1560 38.8 125 38.4 .
Medical Attention for Nephropathy in the last 12 months 1607 83.1 126 90.5 85.3
1 > 1 % A b o v e N a t i o n a l A v e r a g e
2 < 1 % B e l o w N a t i o n a l A v e r a g e
3 + / - 1 % o f N a t i o n a l A v e r a g e
dbhat
Diabetes Management Pre-BPA Implementation vs. Post-BPA Implementation
3.0 0.0180 * 89.3 1.9 0.4147 62.4 0.1 0.7875 . 6.4 <0.0001 * 85.9 3.8 0.0194 * 48.2 0.1 0.5224 . 0.7 0.1083 95.0
-1.2 0.4101 33.6 -1.5 0.3285 . 4.2 <0.0001 * 85.3
Pre-BPA Implementation Period: Jul-2010 to Jun-2011 Post-BPA Implementation Period: Jul-2011 to Jun-2012
* P < 0.05 for differences between post-BPA (exceptions considered) and pre-BPA outcomes.
Please note: BP measurement and control were not included in the project-related interventions.
NCQA National Average (Pre- BPA Period)
BP < 130/80mmHg (denominator: all diabetics with non-missing values) 1560 38.8 1717 37.6
LDL-C < 100mg/dl (denominator: all diabetics with non-missing values) 1313 67.6 1526 67.8 BP measured in the last 12 months 1607 97.1 1756 97.8
Medical Attention for Nephropathy in the last 12 months 1607 83.1 1756 86.4
BP < 130/80mmHg (denominator: all diabetics) 1607 37.7 1756 36.7
LDL-C measured in the last 12 months 1607 81.7 1756 86.9 LDL-C < 100mg/dl (denominator: all diabetics) 1607 55.3 1756 58.9
HgbA1c < 8% (denominator: all diabetics) 1607 70.2 1756 71.9 HgbA1c < 8% (denominator: all diabetics with non-missing values) 1421 79.4 1586 79.6
Pre-BPA Post-BPA (No Exceptions
% Criteria Met
Number Eligible
% Criteria Met
91.4HgbA1c measured in the last 12 months 1607 88.4 1756 90.3
1729 87.3
Difference in % Completion Rates: Post-BPA (Exceptions Considered) - Pre-BPA
P-value
Post-BPA (Exceptions Considered)
Number Eligible
% Criteria Met
Number Eligible
% Criteria Met
1543 79.5 1477 81.2 3.3 0.0050 * .
1543 79.5 1472 81.5 5.5 0.0005 * 88.4 1543 57.5 1472 59.3 2.8 0.1166 57.6 1226 72.3 1186 73.6 -0.7 0.7370 .
1543 95.9 1489 95.8 0.7 0.4094 95.0 1543 69.4 1489 69.6 -0.2 0.9244 .
1480 72.4 1427 72.6 -0.8 0.6426 .
343 73.5 328 76.5 8.7 0.0012 * 77.9 572 79.0 542 81.4 3.9 0.0633 .
Pre-BPA Implementation Period: Jul-2010 to Jun-2011 Post-BPA Implementation Period: Jul-2011 to Jun-2012
* P < 0.05 for differences between post-BPA (exceptions considered) and pre-BPA outcomes.
Please note: BP measurement and control were not included in the project-related interventions.
CHD: Antiplatelet Therapy 1385 77.9 CHD: LDL-C measured in the last 12 months
Pre-BPA
CHD with DM: ACEi/ARB Therapy 489
CHD: LDL-C < 100mg/dl (denominator: all CHD patients with non-missing values) 1052 CHD: BP measured in the last 12 months 1385 CHD: BP < 140/90 mmHg (denominator: all CHD patients) 1385 CHD: BP < 140/90 mmHg (denominator: all CHD patients with non-missing values) 1317
CHD: LDL-C < 100mg/dl (denominator: all CHD patients) 1385
73.4 CHD with MI: Beta Blocker Therapy 292 67.8
77.5
69.8 95.1 74.3
Post-BPA ( Exceptions Considered)
Post-BPA (No Exceptions
Measure Number Eligible
% Criteria Met
Number Eligible
% Criteria Met
Screening Mammography, women ages 40 to 69 years 3409 71.5 3646 69.8 3579 75.5 4.0 <0.0001 * 70.4 Pneumococcal Vaccination, ages 65+ years 4163 66.7 4868 72.8 4787 81.3 14.6 <0.0001 * 82.0 Colorectal Cancer Screening, ages 50 to 75 years 5748 70.7 6377 73.9 6330 84.0 13.3 <0.0001 * 58.3 Osteoporosis Screening, women ages 65+ years 2439 72.4 2855 76.4 2804 79.9 7.5 <0.0001 * 68.0 Cervical Cancer Screening, women ages 21 to 64 years 3204 53.6 3366 53.7 3285 62.3 8.7 <0.0001 * 74.2
Pre-BPA Implementation Period: Jul-2010 to Jun-2011 Post-BPA Implementation Period: Jul-2011 to Jun-2012
* P < 0.05 for differences between post-BPA (exceptions and overrides considered) and pre-BPA outcomes.
Post-BPA (Exceptions Considered
Difference in % Completion Rates: Post-BPA (Exceptions Considered and HM Overrides Considered) - Pre-BPA
P-value NCQA National Average (Pre-BPA Period)
Pre-BPA Post-BPA (No Exceptions
dbhat
Attachment 2 contd.
Fishbone Analysis of the possible reasons for missing HgbA1c and LDL-c tests among diabetics
Pareto Chart analyzing the reasons for missing HgbA1c and LDL-c tests among diabetics using data collected by manual chart review.
dbhat
ONLY
Mis-Fire, ie why did BPA appear?
Patient Removed from BPA QI Data
Permanent Exemption
BPA Off
Data
Patient Remains in
Managed by other MD
This POS Visit Not Included in MDs QI
Data
Examples:
Data
Period
Attachment 2 Post-BPA Analysis of Diabetes, CHD, and Preventive Screening Outcomes
PostBPA_DiabetesOutcomes
Attachment 4 Decision Tree
CSE Abstract UTSW Epic IT BPA project-3.pdf
Submission is limited to a maximum word count of 1500 (not including text in graphs).