1. Partnering for Maternal Data Quality Improvement
Elliott Main, MD: CMQCC Medical Director
Anne Castles, MPH, MA: CMDC Project Manager
Barbara Murphy, RN, MS: CMQCC Administrative Director
Supported with grants from:
Centers for Disease Control
California Health Care Foundation

2. Presenter Disclosure(s):
Objectives:
Describe the initiatives in California to improve BC data quality
Describe the national multi-organization effort to standardize maternity terminology
Describe the importance of sharing data in order to improve the data quality
Presenter Disclosure(s):
None

3. CMQCC and CPQCC Mission: Improving care for moms and newborns
California Maternal Quality Care Collaborative (CMQCC)
Expertise in maternal data analysis
Developer of QI toolkits
Host of collaborative learning sessions
California Perinatal Quality Care Collaborative (CPQCC)
Expertise in data capture from hospitals
Established secure data center
Data use agreements in place with 130 hospitals with NICUs
Model of working with state agencies to provide data of value
Both are Multi-Stakeholder Public/Private Quality Collaboratives
With DPH playing a leading role

4. The California Maternal Data Center
(CMDC) Project Vision
Build a statewide data center to collect and report timely maternity metrics—in way that is low cost, low burden and high value for hospitals
Produce metrics that will support QI and L&D service line management
Improve quality of administrative data
Facilitate reporting to national performance organizations
Over time, publicly report select set of robust measures to inform decisions of childbearing women
Low Cost: The Maternal Data Center is being financed in its beginning stages by both the CDC and the California HealthCare Foundation. Because of this funding, it is anticipated that, for at least the next 2 years, there will be no hospital participation fees.
Data Quality:
There’s no reason to duplicate data reporting efforts when most of the required data is already being captured via administrative data sets such as OSHPD PDD and the birth certificate data.
We know admin data is not perfect, but our philosophy is to work with hospitals to improve the accuracy and completeness of those data sets, rather than setting up a whole new, duplicative data reporting effort.
As part of this initiative, want to provide tools to assist with data quality improvement, as well as clinical quality improvement.
Over time, as hospital have chance to work on both clinical and data quality, work towards publicly reporting a select set of measures, much as CMS is already doing with the Medicare-related perform metrics. The goal is to provide childbearing women with high quality information with which to base their health care decisions.

5. PDD--Discharge Diagnosis File Birth Certificate File
CMQCC Maternal Data Center: Data Flow
PDD--Discharge Diagnosis File
(ICD9 codes)
Birth Certificate File
(Clinical Data)
Many IRBs!
Uploads electronic files
Links Birth Data to OSHPD file
Runs exclusions
3. Identifies CS and Inductions
4. Prints list of charts for review
CMQCC Data Center
Web-based Client-Server Application (secure)
Local encryption of protected health information (PHI)
This approach is very similar to that taken by NSQIP (National Surgical Quality Improvement Program)
Hospital uploads data to CMDC and then can see their own data with PHI
This allows them to add data on individual patients
The CMQCC server and staff do not have access to un-encrypted PHI such as name, medical record number (MRN)
<39wk Elective Delivery CHART REVIEW
Labor?/SROM? (~6% of cases for brief review)
Limited manual data entry for this measure
REPORTS Benchmarks against other hospitals
Sub-measure reports
Calculates all the Measures
Mantra: “If you use it, they will improve it”

6. Why Improve Maternity Data?
HISTORICALLY: Maternity data in PDD and BC used by researchers and public health professionals to track trends and practices
NOW: An additional focus on evaluating and improving the quality of maternity services
CMS Inpatient Quality Reporting Program: reporting of ED<39 weeks to start in 2013
Medi-Cal: Developing quality dashboard; likely to include perinatal metrics
TJC to require reporting of perinatal set for hospitals that perform more than 1100 deliveries annually: to start 2014
QI Collaboratives: Patient Safety First and HENs :

7. How Many Horses Are At Your Data Trough?

8. Key Principle: The more users for the data, the greater the effort for improving data quality.

9. Maternal Data QI in California: 5 Components
Standardize Definitions
Education (providers and staff)
Redesign / System Changes
Improving Data as QI Project
Create Value for Maternal Data QI for hospitals

10. Maternal Data QI in California: 5 Components
Standardize Definitions
reVITALize Project
GA Toolkit (work with ACOG and Hospitals)

11. Obstetric Data Definitions Initiative
National Conference
August 2-3, 2012
Arlington, Virginia

12. Campaign Initiatives
To nationally standardize obstetric clinical data definitions.
To educate and advocate for national implementation of the standardized obstetric data elements and definitions in electronic medical records, birth certificates, and data registries
To increase and improve performance measurement and implementation of the national obstetric data standards and encourage data aggregation.

13. In addition to primary funding from ACOG, we had the financial support of the March of Dimes, SMFM, and United Health Foundation.
As well as the volunteer hours, expertise, dedication of individuals from a variety of organizations including federal agency partners.
Over 80 national leaders in women’s health care with the common goal of standardizing clinical obstetric data definitions for use in registries, EMRs, and vital statistics

14. It’s the Language… The World of Clinical Obstetrics
The World of Public and Admin Health
Communications
“Britain and America are two nations divided by a common language.” George Bernard Shaw

15. It’s the Language…
Different regions of the country may use terms differently …
Even within an OB department, not everyone uses the same terms for the same condition…
Different notes on the same patient, can have different terms used (induced vs augmented)
Birth clerks and coders have to read the notes and then….guess? And then translate into their categorical systems

16. Pre-Conference Preparation
Timeline 1
Pre-Conference Preparation
Identified data elements from various sources, including:
2003 Birth Certificate
ACOG/National Committee for Quality Assurance/Physician Consortium for Performance Improvement – Maternity Care Set
Agency for Healthcare Research and Quality – Birth Trauma Injury Rate
California Maternity Quality Care Collaborative – Healthy Term Newborn
The Joint Commission – Perinatal Care Core Set
Two rounds of surveys were completed by conference stakeholders to determine necessity of revision and priority
Provided existing definitions from ACOG, ICD-09, Williams Obstetrics, the National Center for Health Statistics, and others to serve as a basis for revision discussion

17. Conference Proceedings2
Conference Proceedings
Over 80 national leaders in women’s health care with the common goal of standardizing clinical obstetric data definitions for use in registries, EMRs, and vital statistics
69 individual data elements up for consideration
Attendees were divided into five workgroups to facilitate discussion and formulate revised definitions
53 revised data element definitions were developed and voted on by conference attendees
44 revised data element definitions received > 85% ‘Support’ votes
9 failed to reach 85% ‘Support’ and required additional revision
Five workgroups = Labor, Delivery, Gestational Age & Term, Maternal Indicators Historic, Maternal Indicators Current
Describe the hours of deliberation and voting

18. Post-Conference Follow-Up3
Post-Conference Follow-Up
The data element definitions not reaching 85% of attendee support were brought back into workgroup conference calls for additional discussion and revision
50 refined data element definitions were sent forward for Public Comment
Public Comment was open NOV 2012 to JAN 2013
625 individuals, representing over 450 organizations participated
Nearly 11,000 responses were received in support of the revised definitions
Public Comment Review and Finalization (In Progress)
Public Comment Process

19. Implementation (In Progress)4
Implementation (In Progress)
Publications
Data Dictionary
Articles
Education
Incorporation into Integrating the Healthcare Enterprise (IHE) profiles for EMR certification and Meaningful Use
Clinical decision support
EMR Patient Management Triggers
Data quality auditing logic models
Incorporation into coding and nomenclature
What is next?

20. Thank You, Workgroup Leaders!
Jennifer Bailit, MD, MPH, FACOG
Kimberly Gregory, MD, MPH, FACOG
Cleveland, OH
West Hollywood, CA
Debra Bingham, DrPh, RN
Washington, DC
Tina Groat, MD, MBA, FACOG
Canton, MI
Gerald Carrino, PhD, MPH
White Plains, NY
Isabelle Horon, DrPH
Baltimore, MD
Suneet Chauhan, MD, FACOG
Norfolk, VA
David LaGrew, MD, FACOG
Fountain Valley, CA
Rebekah Gee, MD, MPH, FACOG
Baton Rouge, LA
David Lakey, MD
Austin, TX
Pause on this slide.
Summarize main objectives
Why data standardization is important
Multistakeholder, multidisciplinary collaborative process with 60 day Public Comment of reVITALize
Thanks to those who: participated in Public Comment,
attendees who came to reVITALize,
operational and clinical leaders who helped shepherd us through the process
everyone in the audience, who will help disseminate, implement and incorporate them into practice documentation
William Sappenfield, MD, MPH
Tallahassee, FL

21. Maternal Data QI in California: 5 Components
Standardize Definitions
Education: Incent complete and accurate documentation among
Providers
Coders
Birth Clerks
Birth Data Quality Training Sessions (Vital Records)
GA Toolkit (CMQCC, ACOG and Hospitals)

22. Findings for Key Fields: NTSV* CS
Data Element
Average Missing1
Average Missing or Inconsistent2
Birthweight
0.0% --
Delivery Method
0.7%
OB-GA
0.2%
LMP
2.2%
Presentation
4.6%
10.1%
Parity
0.3%
*Nuliparous, Term, Singleton, Vertex; aka Low-risk, First Birth CS (HP 2020, Joint Commission, CMS measure)
1CA BC data from Jan--Sep 2012 statewide all births
2CA PDD vs. BC from Jan–Dec 2011 statewide all births

23. Conclusions for Key Fields
Definite Area for Improvement
Fetal Presentation
Even though clerks score in medium range for ease of finding and frequency of contradictory information, large percentage of actual missing /contradictory suggest a problem
When asked to code less common clinical terminology (e.g. Occiput Anterior), BC clerks picked right answer only 14-31% of time. (Report p. 6)

24. Conclusions for Key Fields
Fetal Presentation Terminology
Vertex = Occiput = Cephalic
OA, OT, OP Deep transverse arrest Face or compound
Presentation may be missing for CS deliveries

25. Birth Data Quality Improvement Project
2012

26. Tool Kit & Tip Sheets Agenda & Letter from CMQCC & SCCPHD
AVSS Data (January-June 2012)
Tip sheets
Worksheet
CD of all files and tip sheets
Contact information

27. Maternal Data QI in California: 5 Components
Standardize Definitions
Education
Redesign / System Changes: Improve clinical documentation systems across hospital providers to facilitate complete and accurate data capture
Standard Locations for Key Data
Work with EMR vendors to generate worksheet
Develop and disseminate coding best practices
GA Toolkit (CMQCC, ACOG and Hospitals)

28. The California Birth Clerk Survey
Goals
Assess current data collection practices and challenges for key data elements
Identify potential strategies for facilitating accurate data capture by birth clerks

29. Data Issues for Gestational Age
No EDD or GA in Doctor’s note(s)
Multiple EDD / GA’s in L&D chart (which is best?)
Multiple EDD’s in Prenatal chart (which is best?)
Transcription errors when copying from prenatal
Delivery occurring many days after the admission GA
Revision of EDD / GA after admission
Lack of a standard approach for using US to confirm/establish best EDD.
Low Cost: The Maternal Data Center is being financed in its beginning stages by both the CDC and the California HealthCare Foundation. Because of this funding, it is anticipated that, for at least the next 2 years, there will be no hospital participation fees.
Data Quality:
There’s no reason to duplicate data reporting efforts when most of the required data is already being captured via administrative data sets such as OSHPD PDD and the birth certificate data.
We know admin data is not perfect, but our philosophy is to work with hospitals to improve the accuracy and completeness of those data sets, rather than setting up a whole new, duplicative data reporting effort.
As part of this initiative, want to provide tools to assist with data quality improvement, as well as clinical quality improvement.
Over time, as hospital have chance to work on both clinical and data quality, work towards publicly reporting a select set of measures, much as CMS is already doing with the Medicare-related perform metrics. The goal is to provide childbearing women with high quality information with which to base their health care decisions.
No wonder a Birth Certificate clerk may have difficulties!

30. Mandated Reporting of Maternity Measures
Organization
Measure
BC Data Elements NOT in PDD (ICD9 codes)
CMS
Early Elective Delivery
Best OB GA
The Joint Commission
NTSV Cesarean Section
Best OB GA, Parity
Antenatal Steroid
Neonatal Sepsis
Birth Weight
Exclusive Breast Milk Feeding
Again…Best EDD  Best OB GA is the critical BC data element for QI

31. Consensus for Identifying Best EDD
Spong CY. Defining “Term” pregnancy: Recommendations from the defining “term” pregnancy workgroup. JAMA 2013 May 3:1-2. [E-pub of print]

32. Most Important Single Data QI Project is: Best EDD
Medical Policy Issues
When to change Best EDD based on US
Criteria for US (sac <7wks, CRL <14wk, BPD <20wk)
How to reconcile multiple US reports
Need to improve wording of US reports for EDD
Implementation/Process Issues
Prenatal Best EDD Black Box on every record
To be completed by 20 weeks
Tweak new AMA/PCPI OB quality measure to capture this
On admission, this EDD is transferred to a similar Hospital Best EDD Black Box used by all (including BC clerks)

33. Maternal Data QI in California: 5 Components
Standardize Definitions
Education
Redesign / System Changes
Improving Data as QI Project: Apply QI principles to improving accuracy/completeness of data
Hospital-BC Missing Data Reports
Comparisons of BC to PDD for audit and feedback
QI Run Charts for Data Quality

34. Data Quality Reports
Identify discrepancies or missing data in Birth Certificate and Discharge data files
Use to target data performance/quality improvement
Screen shot from the California Maternal Data Center

35. 35

36. --Each individual hospital within Riverside County--
Hospital Alpha with high rate
--Each individual hospital within Riverside County-- 36

38. A hospital with a system for transferring clinical data to the BC38

39. Maternal Data QI in California: 5 Components
Standardize Definitions
Education
Redesign / System Changes
Improving Data as QI Project
Create Value for Maternal Data QI for hospitals (“If you use it, they will improve it”)
Use BC/PPD data for internal QI
Use BC/PPD data for QI reporting (e.g. to TJC)
Use BC/PPD data for Public Reporting

40. New Joint Commission Decision
As of July 2013, hospitals have option to use Birth Certificate data for TJC Perinatal Set for:
OB Estimate of GA
Birthweight
Parity
Implication  Abstract one time (well!) and satisfy both Vital Records and TJC requirements
Potential to make hospital data collection and reporting activities more efficient
Improves quality of data for use by state policymakers

41. Data Quality Improvement
Birth Clerk education (Tip Sheets, classes)
Strategies for improving provider documentation
Consensus on using US to confirm/establish Best EDD (ACOG, IHI)
Require GA/Best EDD at Delivery note
Standard black-bordered box for Best EDD on Prenatal and L&D admission forms
Harness EMR system(s) in a structured manner (remember GIGO)
Use Audit and Feedback to improve data
Time-course run-charts
Drill down to individual cases for careful analysis
Comparison to others
Accountability!!
Low Cost: The Maternal Data Center is being financed in its beginning stages by both the CDC and the California HealthCare Foundation. Because of this funding, it is anticipated that, for at least the next 2 years, there will be no hospital participation fees.
Data Quality:
There’s no reason to duplicate data reporting efforts when most of the required data is already being captured via administrative data sets such as OSHPD PDD and the birth certificate data.
We know admin data is not perfect, but our philosophy is to work with hospitals to improve the accuracy and completeness of those data sets, rather than setting up a whole new, duplicative data reporting effort.
As part of this initiative, want to provide tools to assist with data quality improvement, as well as clinical quality improvement.
Over time, as hospital have chance to work on both clinical and data quality, work towards publicly reporting a select set of measures, much as CMS is already doing with the Medicare-related perform metrics. The goal is to provide childbearing women with high quality information with which to base their health care decisions.

42. A boy and his Killer Whale…
Timely sharing of Vital Record data with partners invested in improving data quality is a winner for everyone!
FREE the DATA

43. Thank You!