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Quality Council 2017
OR Anesthesia
Preoperative clinic/CCT
Critical Care
Echocardiography/Pacemaker service
Acute pain service/Regional
Comprehensive Pain Medicine
Center
UCLA Community ASCs
Community Pain Centers
Informatics and Analytics Division
Remote/ Tele Pre-op
West LACommunity
Anesthesiology & Perioperative Medicine: Clinical Services
MLK HospitalOlive View
VA Medical Center
Main Strategies and Operations• Perioperative Care Redesign
• Bring Expertise for Patients Safety, Efficiency, Quality, IT, Metrics from the Intraoperative Period to the Perioperative Period
• Develop a Rationale and Systematic Implementation of Quality Improvements Processes
• Main focuses on:• 1. Developing the TEAM culture and multidisciplinary projects with Departments across
the Healthcare System
• 2. Care coordination of the surgical patients
• 3. Leveraging Technologies to Improve Quality of Care
4
REDUCING RISK-ADJUSTED
MORTALITY
••Complex Care Team
••Rapid Response
IMPROVING PROCESS AND
OUTCOMESMEASURES
••PEPC
••Periop Pathways
••Enhanced Recovery After
Surgery
IMPLEMENTING VALUE-BASED
REDESIGN
••CCJR••Enhanced Recovery After
Surgery••Cataract
Surgery: Preopredesign
••Pre-habilitation Programs
ENHANCING THE PATIENT EXPERIENCE
••PEPC
••Telemedicine
••Regional Service
REDUCING PREVENTALBE READMISSIONS
• CCJR
• Enhanced Recovery After
Surgery
STRENGTHENINGPATIENT SAFETY
••eARS System
••Peer Support
••Safe Handoff
Anesthesia Quality Council
Quality Improvement & Innovation Team Initiatives
Slide adapted from UCLA MOVERS strategy
Clinical Operations
MD Champions:
N. Kamdar; A Edwards A Dhillon
MD Champions:
N. Kamdar
MD Champions:
M. CannessonV. Duval
MD Champions:
S. Rahman M. Ferrante
V. Duval
MD Champions:
V. Duval A. Dhillon
MD Champions:
K. Kuchta,E. Methangkool
Alignment with Nursing & Interdisciplinary teams
Surgical Service Partnerships ValU Team, QMS, Performance Excellence
Data Analytics (Bioinformatics, Hospital QIA, Decision Support)Division Chiefs
Site Directors
Perioperative Models of Care
6
Our Model for the Search of Value
7
8
Differentiating Complexity to Maximize Throughput
9
Mortality ReductionOutcome Improvement
Value-Based Care
CCT
Complex Care Team10
Dr. Nirav Kamdar Dr. Alex Edwards
11
UCLA Co-management by Complex Care Team
0
2
4
6
8
10
LOS
Traditional care
Complex Care Team
190 ASA 3/4 patients managed by the Complex Care Team with-
Decrease in length of stay (LOS) from 9.2 days to 5.4 days (56% reduction)
The average case delay was reduced by 28%.NO same day of surgery cancellations.
Day
s
12
Focus on Complexity in and out of the OR
13
Outcome: 18% De-escalation from ICU Care
14
Outcome: AKI is our largest complication
15
PEPC
Preoperative Evaluation and Planning Center
16
Evolution of Preoperative Evaluation
Gather and summarize all available and relevant
informationOptimize OR throughput
Identify high risk patients
Coordinate preoperative process
• Recommendations for perioperative care• Reduce silo-driven care• Early involvement of operative and perioperative
teams
Understand the patient’s goals, needs, values, lifestyle, and make their health care work within that framework
Prepare patients for surgery• Allergy de-labeling• OSA Dx & Rx• Smoking cessation • Obesity Management
Improve overall health• Quality of life• Behaviors• Medical conditionsNe
xtPh
ase:
Pr
ehab
ilita
tion
Rec
ent
Prog
ress
Orig
inal
D
esig
n
17
Can
cella
tion
rate
(%)
Outcome: PEPC reduces Surgical Cancellations
Average: 2.8%
Average: 6.4%
18
SMART Screen: Maximizing our LEAN Process
• Algorithms implemented as pilot on MP200 GI Suite • Pilot launch in SEI – Nov 2017
• Identify those patients likely to benefit from MOC screening based on
18
Decision Support Screening CriteriaDiabetes ESRD
CAD Elevated BMICHF Previous ASA Score >3
Fewer than 2 previous visits to UCLA
AUTOMATEDASSESSMENTOFPATIENT'SREVISEDCARDIACRISKINDEXUSINGALGORITHMICSOFTWAREHoferI,ChengD,FujimotoY,Cannesson M,MahajanA
SMART Screen: Improves our Bandwidth
19
20
CJR
Comprehensive Care for Joint Replacement
Dr. Neesa Patel Dr. Natale Naim
21
CCJR: Protocols for Predictable Outcomes
21
Pre-Op Intra-Op Post-Op
TelemedicineConsult
• History Gathering
• Expectation Setting & Education
• Regional Consent
Regional Anesthesia
• Adductor Canal Block
• Spinal Anesthetic
Improved Recovery
• mLOS = 2.66
• Time to mobility
22
CCJR: Focus on LEAN operations using analytics
Outcome: Reducing LOS within 1 Quarter
23
2.4
2.5
2.6
2.7
2.8
2.9
3
3.1
3.2
Sep Aug Oct Nov
LOS
Data courtesy of
24
Enhanced Recovery After Surgery Collaborative
Dr. Aviva RegevCarol Lee, RN-BC
Dr. Siamak Rahman
25
Special thanks to our multispecialty partnerships Dr. Lin, Sack, Kazanjian, Hallie Chung, RN
Dr, Litwin, Chamie, SaigalDr. Cohen
NursingPharmacy
376 patientstreated by our ERAS
teams (Nov 2017)
Outcome: Reduced LOS, Opioids & PONV
26
Post ERASJune 2016- July 2017
7.5%Reduction in LOS in 1 year
for Non-IBD ERAS-Colorectal patients
PONV (Median)
33%Reduction in
27
Patient ExperiencePatient Satisfaction
Wellness Bundle : Health System Alignment
• Comfort• Opioid sparing strategies• Early recognition of high risk patients & interventions by pain management services
• Nutrition• Optimal fasting times• Pre-op Carb loading beverage • Gum chewing/sham feeding
• Mobility• Adequate pain management to promote early ambulation
28
Sample department wide campaign
Telemedicine: Growing numbers and satisfaction
29
“Convenient and saved me 120 miles of driving. It beats driving down [to Westwood] in traffic.”
30
Pain ManagementMultimodal Analgesia
& Opioid Sparing Techniques
Dr. Neesa Patel Dr. Siamak Rahman
31
020406080
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/201
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Orthopedic ServiceBlocks Placed Per Month at SMH
00.5
11.5
22.5
33.5
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Aver
age
LOS
in d
ays
Average LOS in Days for All Orthopedic Patients at SMH
Ortho Patients With Blocks Linear (Ortho Patients With Blocks)
Regional Block and Ortho LOS at SM93 % of patients extremely satisfied with post op pain
management
> 400 ambulatory nerve catheters in 2016
32
Total Blocks – SM OR
*
* Blocks calculated until 11/21/17
33
Patient SafetyData Driven Improvement…
A Focus on Patient and Provider
eARS: Improving patient safety reporting
• Mandatory Reporting for Quality Improvement & Safety• External to Epic to comply with confidentiality
34
eARS: Data driven patient safety impact
35
Monthly Average~2% incidence of reported events
141 reported QI events 121 cases with events
Peer Support – Another Anesthesia First
Peer Support Project
conception collaboration
November 2017
VolunteerPeer Supporter
Training
December 2017
Peer Support and Burnout Survey
December 18th
Official Launch Peer Support Program
Pilot
2018: UC Health
SystemwidePeer Support
Program
20152017
20172017
2018
37
InformaticsProviding Operations Analysis to our Collaborators
Copyright © 2016 International Anesthesia Research Society. Unauthorized reproduction of this article is prohibited.Copyright © 2016 International Anesthesia Research Society. Unauthorized reproduction of this article is prohibited.1880 www.anesthesia-analgesia.org June 2016 ڇ Volume 122 ڇ Number 6
The 2009 American Recovery and Reinvestment Act cre-ated “meaningful use criteria” for the adoption and implementation of electronic medical records (EMRs)1
assuming that increased adoption of EMRs would improve the quality of care and reduce costs.2,3 Unfortunately, these cost savings have yet to be realized,4 and some have found that EMRs have paradoxically increased the cost of care by allowing improved billing capture.5,6 One barrier to improved care quality and associated cost savings is the difficulties asso-ciated with turning EMR data into actionable information that can be used to improve health care delivery and outcomes.7
The transition from volume-based payments to value-based payments encouraged by the Affordable Care Act as a way to realize these savings requires consistent and reliable extraction of data from the EMR for both measurement and use in quality improvement programs.8 The Perioperative Surgical Home,9 the American Society of Anesthesiologists’ implementation of an accountable care organization, specifically targets getting these data10 through the early use of a data registry as part of its rollout. Despite the urgency of this need, as well as signifi-cant effort, these data remain difficult to obtain.
Currently, EPIC Systems’ EMR is the largest EMR plat-form, with more than half of the US population now having
a patient record in an EPIC system.11 Although there has been some success in extracting data into uniform data models from other systems,12 EPIC EMRs have been par-ticularly challenging, given their expansiveness and large number of tables (>15,000).
A well-established method to combine disparate, and often unstructured, data in such a way as to make it more easily accessible to the end users is to create a data warehouse (Appendix 1). We present our experience and describe the methodology for successfully extracting clini-cal data around the entire perioperative period from our EPIC EMR (Epic Systems, Verona, WI) into an indepen-dently designed data warehouse designed for business intelligence that simplifies access to the data and standard-izes definitions so as to allow multiple groups to report off of the same data.
METHODSAfter obtaining exemption from informed consent from the University of California, Los Angeles IRB, a review of clinical and operational metrics desired for reporting was undertaken. The necessary raw clinical data were located in Clarity, the relational database created by EPIC for data analytics and reporting. Given the array of metrics needed and the complexity of the data structure in Clarity, a 2-stage data warehouse was constructed to reduce the need to join and optimize multiple tables.
The first stage, termed “Base Tables,” was designed to serve as a middle layer decreasing the number of tables and simplifying the joins between them. Conceptually, the tables coalesced into 3 groups: (1) patient-centered information (laboratories, allergies, medial history, etc.); (2) encounter- centered information (admission, discharge, and transfer [ADT], orders, notes, laboratories, etc.); and (3) operative procedure-centered information (staffing, scheduling, room times, etc.). Tables are joined by 1 of 3 of the following fields: (1) a patient identifier (pat_id); (2) a case identifier (case_id); or (3) an encounter identifier (enc_id). In creating these tables,
Copyright © 2016 International Anesthesia Research SocietyDOI: 10.1213/ANE.0000000000001201
Extraction of data from the electronic medical record is becoming increasingly important for quality improvement initiatives such as the American Society of Anesthesiologists Perioperative Surgical Home. To meet this need, the authors have built a robust and scalable data mart based on their implementation of EPIC containing data from across the perioperative period. The data mart is structured in such a way so as to first simplify the overall EPIC reporting structure into a series of Base Tables and then create several Reporting Schemas each around a specific concept (operating room cases, obstetrics, hospital admission, etc.), which contain all of the data required for reporting on various metrics. This structure allows centralized definitions with simplified reporting by a large number of individuals who access only the Reporting Schemas. In creating the database, the authors were able to significantly reduce the number of required table identifiers from >10 to 3, as well as to correct errors in linkages affecting up to 18.4% of cases. In addition, the data mart greatly simplified the code required to extract data, making the data accessible to individuals who lacked a strong coding background. Overall, this infrastruc-ture represents a scalable way to successfully report on perioperative EPIC data while standard-izing the definitions and improving access for end users. (Anesth Analg 2016;122:1880–4)
From the Departments of *Anesthesiology and Perioperative Medicine and †Medicine, David Geffen School of Medicine at UCLA, Los Angeles, Califor-nia; and ‡Office of Health Informatics and Analytics, David Geffen School of Medicine at UCLA, Los Angeles, California.Accepted for publication December 23, 2015.Funding: None.The authors declare no conflicts of interest.Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (www.anesthesia-analgesia.org).Reprints will not be available from the authors.Address correspondence to Ira S. Hofer, MD, Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at UCLA, 757 Westwood Blvd., Los Angeles, CA 90095. Address e-mail to ihofer@mednet.ucla.edu.
A Systematic Approach to Creation of a Perioperative Data WarehouseIra S. Hofer, MD,* Eilon Gabel, MD, MS,* Michael Pfeffer, MD,† Mohammed Mahbouba, MD, MS,‡ and Aman Mahajan, MD, PhD*
E TECHNICAL COMMUNICATION
Copyright © 2016 International Anesthesia Research Society. Unauthorized reproduction of this article is prohibited.Copyright © 2016 International Anesthesia Research Society. Unauthorized reproduction of this article is prohibited.June 2016 ڇ Volume 122 ڇ Number 6 www.anesthesia-analgesia.org 1883
Creation of a Perioperative Data Warehouse
The increasing use of data to drive organizational deci-sions has created the need to develop tools allowing those with less technical knowledge access to increasingly sophis-ticated data. Traditional methods or having a report writer create separate reports for each business case can be quite cumbersome, has long turnaround times, and requires a strong background in database query and design. The goal of this data warehouse was to make these data accessible to those who might lack these skills.
The metrics contained in the Reporting Schemas were dictated by organizational needs. Once a metric was defined, it was reported on for all operative cases, not just those in a specific cohort of cases (as would be the case with traditional independent queries for each report). Instead, the scope of the report is limited by the report writer using their analytics soft-ware. This results in an ever increasing library of metrics for reporting and a dramatic decrease in the technical skill needed to generate reports, as seen in Table 2 and Supplemental Digital Content 2 (http://links.lww.com/AA/B369).
The limitations of creating this data mart reflect the underlying complexity and fluidity of the EPIC data
structure. First, because each EPIC implementation is different, the overall structure and concepts described here can be replicated at another institution, but the detailed code and data validation would need to be developed based on the workflow at that institution. Second, although the final data extraction from the Reporting Schema may be straightforward, implementa-tion of the data mart requires personnel who have the technical ability to build a database and enough clini-cal knowledge to help drive the metric creation and data validation. These resources may be beyond the scope of smaller institutions.
The rapid adoption of EMRs over the past 5 years, combined with the transition to a value-based model of care, has resulted in a rapidly growing need to improve extraction of data from the EMR. The data model pre-sented here has provided the authors and their institu-tion with a dramatically improved ability to rapidly find and report on all aspects of the perioperative encounter, and, thus, it is a valuable tool in improving perioperative patient care. E
Figure 2. A visual depiction at the tables involved in calculating case cancellation data at each state of the data warehouse.
The Perioperative Data Warehouse (PDW)
PDW Reporting• Nearly 800 highly validated metrics
• LOS• OR Volume• Readmission• Clinical Outcomes
• Renal Failure
Dashboards: Individual insights – Systemic Success
40
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