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CSLC Case Study
“You Can’t Make Good Decisions With Bad Data”
Data Validation Initiatives at West County Health Centers
A Meeting of the Clinical Systems Learning Learning Community
Tuesday 8/21/2007
CSLC Case Study WCHC - 04/25/07 2
Introducing West County Health Centers
Clinical Sites: Russian River Health Center - Guerneville, CA Occidental Area Health Center – Occidental, CA Teen Clinic – Forestville, CA
Number of Staff (total), Number of Providers 75 employees in five locations; 58 FTEs 22 providers (including 3 dentists, 4 mental health counselors and 1 psychiatrist); 14 FTEs
2006 UDS data about visits/patients 8,150 patients 36,799 visits
Primary Care Services – Newborns to Elders: Pre-natal and Obstetrical Care Well Child Exam and Low Cost Immunizations School and Sports Physical Exams Routine, Annual and Employment Physicals Reproductive Health Care HIV/AIDS Primary Care Child Health and Disability Program (CHDP) Breast Canter Early Detection Program (BCEDP)
Special Services: Dental Services HIV Case Management Mental Health Counseling
CSLC Case Study WCHC - 04/25/07 3
Goals of This Presentation
To explore the issues currently present for CSLC members in terms
of Data Validation. To show WCHC’s approach to
“diving” into the data To stimulate discussion about the
best approach to the Data Validation Process
CSLC Case Study WCHC - 04/25/07 4
Technology-Enabled Quality Improvement
Technology-enabled quality improvement allows for more automated and timely data collection and reporting, larger scale initiatives and broader participation without adding resources.
Chart Audits, while highly accurate, are time consuming and resource intensive. Participating in a new disease collaborative often means adding staff.
CSLC Case Study WCHC - 04/25/07 5
A Critical Juncture for Proving HIT Value
As more data is available to providers, we see the “Light Bulb Moment” occurring
Registries have provided an insight into patient population characteristics that was not previously available
Creates a “thirst” for more data – driven internally by providers rather than by external reporting requirements
Sets the foundation for data-informed decision making, as well as EHR implementation and use
Data accuracy in these early stages is critical in building trust Consequences of billing data error vs. clinical data error P4P and Provider Incentives Bad data undermines providers’ trust in technology
As more data collection systems are introduced and interfaced, multiple points of failure are introduced
Reports contain data from multiple sources and systems Process definition, redesign and communication becomes as important as the
technology.
CSLC Case Study WCHC - 04/25/07 6
An Environment in Transition
Paper Charts
Fully Integrated and Interoperable
EHRs
You Are Here
CSLC Case Study WCHC - 04/25/07 7
Accuracy is Fraught with Perils
What Actually Happened
What Appears on Report What Appears on Report
CSLC Case Study WCHC - 04/25/07 8
Data Entry at the Point of Care
The closer the data entry is to the actual clinical event, the less chance of errors and omissions.
No need to navigate the “swamp” of connecting systems and processes.
However, the source document for 90% of the data in health centers is still the Encounter Form (Superbill, Billing Slip).
CSLC Case Study WCHC - 04/25/07 9
Multiple Points of Failure
“Our systems are interfaced; what could go wrong?”
Patient is registered or checks in. Billing
Slip printed.
Patient is registered or checks in. Billing
Slip printed.
Patient is seen and Provider
completes Billing Slip
Patient is seen and Provider
completes Billing Slip
Billing Slip sent to
Billing Dept. for entry to PM System
Billing Slip sent to
Billing Dept. for entry to PM System
Tracking system
receives data
through interface.
Tracking system
receives data
through interface.
Lab or other tracking
data entered into system.
Lab or other tracking
data entered into system.
Data entry errors
Data entered on wrong patient
Duplicate patients in system
Codes are not mapped correctly
Interface program incorrect
Duplicate patients in system
Data entry errors
No patient in tracking system to match result
Missing source data slips
Billing Slip is incomplete
Billing Slip is inaccurate
Billing Slip gets lost altogether
Data entry errors
Data entered on wrong patient
Extra time for errors & missing info
CSLC Case Study WCHC - 04/25/07 10
Principles for Maintaining Healthy Data: S T R I V E
Standards There are well-defined and frequently-measured standards for data integrity Start from the beginning of your “data entry process” AND involve ALL STAFF
Training Training to remediate errors is targeted and frequent, and addresses the standards the
organization has set. Responsibility
Someone is accountable for data stewardship Expectation of and standards for data accuracy are called out in job descriptions Fewer errors occur if the “owner” of the data understands the particular care process
Incentives (and Consequences) Benefits of eliminating waste and rework are shared with the team that meets their goals. There are consequences for data inaccuracy.
Verifiable Understanding what is the source data document or system Testing the systems in a controlled environment Checks and balances: reports from two systems, chart audit
Education End-to-end data flow processes are documented and understood by all Accuracy measurements are posted where all can see and staff can interpret the results Checks and balances: reports from two systems, chart audit
CSLC Case Study WCHC - 04/25/07 11
AGENCY LEADERSHIP
WCHC Executive Director and Management Team committed to the allocation of resources
Training through RCHC: The QCS Series Moving us toward “Culture of Quality”
Training of all Staff at Quarterly Meetings regarding Data Integrity at ALL LEVELS
I2i Tracks- Tool for Accurate Data Collection
System Improvement as Preparation for EMR
CSLC Case Study WCHC - 04/25/07 12
Steps Toward Data Validation
Where to begin? REGISTRATION: Accuracy of Entry PMS Clean up Project CODING: Superbill ACT Project BILLING: Data Entry, Feedback to Providers CLINICAL DATA: Data Stewards Accuracy of Entry and Tracking in i2i Tracks VERIFICATION OF DATA IN REPORTS DATA VALIDATION w Chart Review
CSLC Case Study WCHC - 04/25/07 13
The Source Patient Data Base – Centricity PMRegistration Data
Before Centricity, each clinic had their own separate PM system.
Patients could be seen at either site, and potentially registered twice.
Centricity implementation combined databases, duplicates and all. Over the years, duplicates have been merged but others
created There is now an established process for clinic staff to call
Billing Dept. if duplicates are discovered.
Maria G. Smith Mary Garcia Maria Garcia-Smith Marie Garcia
CSLC Case Study WCHC - 04/25/07 14
The Source Patient Data Base – Centricity PM
Special data quality project was done to systematically review the patient database and merge duplicate patients. 1 Staff Member spent 100 hours merging duplicate files,
inactivating patients and correcting demographic info (27,000 files) 4,000 files became inactive Project included review of front desk procedures and patient
look up to reduce duplicates. Process also included identifying duplicate patients in
i2iTracks and having the vendor merge those patients as well.
Maria Garcia-Smith
CSLC Case Study WCHC - 04/25/07 15
“An Ounce of Prevention”- Superbill ACTCoding Data
For data quality and accuracy, getting the data correct up front is critical
No other document is as all-encompassing and important as the Superbill
• Accurate• Complete• TimelyA C T =A C T =
Problem: Billing slips (Superbills) not being submitted before patient leaves the clinic. Some slips are missing charges, signatures, billing codes and diagnoses. Some slips are missing altogether.
Consequences: Missed fee collection, front office staff searching for slips, Billing Dept. looking for missing information, charges not billed, missing encounter/QI tracking data.
CSLC Case Study WCHC - 04/25/07 16
Superbill Life Cycle
1 2
34
5
6
789
CSLC Case Study WCHC - 04/25/07 17
What We Measured
Superbill on time to check out? (within 2 min. of patient arriving at checkout)
Does Front Office staff have to track down slip?
Completed insurance information on right side of slip? Was sliding scale payment
collected?
Missing charges on Superbill?
Was Pap code circled if Pap done?
Missed information found in billing dept.
Number of Superbills missing altogether at the end of day
CSLC Case Study WCHC - 04/25/07 18
The Estimated Cost of Waste
Missed Charges: Average of 7 Superbills with missed charges per day out of 69 Superbills. Average cost of missed charges = $20
Cost of Missed Charges: $140 x 240 days = $33,600/year
Missing Superbills or 12 in the first four weeks of measurementInaccurate Dx: (Jan.’07), each one $80
Unbilled EncountersPer Month: $960 x 12 = $11,520/year
Approximately 22,000 Superbills per month5.5% rate of missing Superbills
TOTAL: $45,120/year (not including staff time)
Staff Time: Twenty times in six days to track down Superbills and/or information…
CSLC Case Study WCHC - 04/25/07 19
Date Collection Results:Superbills on Time
WCHC SUPERBILL A.C.T.: PERCENT OF ON TIME SUPERBILLS
Avg=84
UCL=103
LCL=64
39
49
59
69
79
89
99
109
1/19am
1/19pm
1/22am
1/22pm
1/23am
1/23pm
1/24am
1/24pm
1/25am
1/25pm
1/26am
1/26pm
1/29am
1/29pm
3/5am
3/5pm
3/6am
3/6pm
3/7am
3/7pm
3/8am
3/8pm
3/9am
3/9pm
4/18am
4/18pm
4/19am
4/19pm
4/20am
4/20pm
4/23am
4/23pm
4/24am
4/24pm
4/25am
4/25pm
Date and Shift
Perc
ent o
f Sup
erbi
lls T
urne
d in
On
Tim
e
AFTERPDSA #1INTERVENTION
BASELINE DATA1/19-1/29
AFTERPDSA #2INTERVENTION
CSLC Case Study WCHC - 04/25/07 20
DATA ENTRY
CSLC Case Study WCHC - 04/25/07 21
CLINICAL DATA ENTRY
Diabetes Care Data Entry Data Stewardship Data Printed on Visit Summary (Double checked by Providers During Visit)
CSLC Case Study WCHC - 04/25/07 22
CSLC Case Study WCHC - 04/25/07 23
CSLC Measures Report – Verifying the Population
CSLC Clinical Measures Report
Item
Value %
1. Diabetic Patient Population - Patients who A.) Had 2 or more ambulatory care encounters during the reporting period or prior year, B.) Have received on two or more different dates of service a diagnosis of diabetes, C.) Were between 18 and 75 years old at the end of the reporting period.
A. Total Patient Count 317 100%
B. Patients with at least 1 HbA1C test in the last year 208 66%
1. Last test < 9% 174 84%
2. Last test < 7% 115 55%
C. Patients with at least 1 LDL-C Test in the last 2 years 238 75%
1. Last test >= 130 50 21%
2. Last test < 130 188 79%
3. Last test < 100 119 50%
Why would there be a discrepancy between the number of patients on the CSLC report vs. the i2iTracks roster of diabetic patients?
CSLC Case Study WCHC - 04/25/07 24
CSLC Measures Report – Verifying the Population
Diabetic Patients in Tracks – Not on CSLC Report
Out of Age Range
(20 / 51%)
Deceased, Still in Tracks (4 / 10%)
Pts. With No DM Coded Visit in PMS Visit (1 / 3%)
Pt. With 1 DM Coded Visit in PMS (14 / 36%)
296 Patients on Tracks Diabetes Roster
3939PatientsPatients
CSLC Case Study WCHC - 04/25/07 25
CSLC Measures Report – Verifying the Population
Diabetes Patients on the CSLC Report – Not in Tracks
Patients Transferred or Inactive (31 / 53%)
Has DM, Not in Tracks (Process Error) (6 / 10%)DM Managed by
Specialist (4 / 17%)
Had DM Dx, Died w/in Past 2 Yrs. (7 / 12%)
Pre-diabetes > 2 Yrs. Ago, No DM Now (7 / 12%)
Gestational Diabetes (2 / 3%)
Had DM Dx, Now Cleared (2 / 3%)
5959PatientsPatients
316 Patients on CSLC Report
CSLC Case Study WCHC - 04/25/07 26
Spreadsheet of Comparisons
COMPARISON OF i2iTRACKS DM PT (MANUALLY ENTERED) VS CSLC REPORT
PTS IN DM TRACKING NOT IN CSLC PTS ON CSLC NOT IN DM TRACKING
4/07=39 8/07=35 1 2 3 4 5 6 7 8 9 10 11
DATE
Out of Age Range Deceased
Only 1 DM Code
NO DM Code
Transferred/ Inactive
DM/ Died in last 2 yrs
Ges tational
DM/ Cleared . 2 yrs ago
DM/ Cleared
Specialist
Has DM/ Not in Tracking
4/12/2007 20 4 14 1 31 7 2 7 2 4 6
8/12/2007 22 0 12 1 39 3 1 0 5 0 2
CSLC Case Study WCHC - 04/25/07 27
Proposed Data Validation Procedures
For Diabetes Tracking1. Establish standardized process for the Billing Department to notify
i2iTracks regarding deceased patients.• Can a “deceased” or “inactive” dummy code be interfaced
from the PM system to Tracks?• Provide access to Tracks from Billing Department?
2. Have Diabetes Case Manager check charts for patients in Tracks with only 1 (or 0)DM code in PM system.
• If seen twice for DM, instruct providers on checking 250 code
• Add codes to PM system historical visits where appropriate• Compare the 2 reports quarterly, follow up on discrepancies
Given the non-duplicated discrepancy of 98 patients between the CSLC Report and i2iTracks, WCHC proposes the following interventions:
CSLC Case Study WCHC - 04/25/07 28
Proposed Data Validation Procedures
For CSLC Report1. Decide as a group if we are going to continue to include those who
have moved or transferred care. For WCHC, this equals 10%.
2. Discuss ways of removing those patients with gestational diabetes or diabetes that clears because of bariatric surgery (or other cause)
3. Discuss report parameters, should report reads “Pts. with any of the 250 diagnosis codes in the past two years and have had two or more ambulatory visits in the past two years”?
4. Clarify if patients being managed by a specialist should be added to the tracking database.
Given the non-duplicated discrepancy of 98 patients between the CSLC Report and i2iTracks, WCHC proposes the following interventions:
CSLC Case Study WCHC - 04/25/07 29
Women’s Health Data
More patients, fewer issues for data validation. Cervical Cancer Screening: 3674 Women between 21-64 w at least 1 visit in 2 yrs
Rate of Pap Smear in 3 yrs=50% 17% of the 3674 were seen just one time 2% obtain GYN care elsewhere Breast Cancer Screening: 2164 Women between 42-69 w at least 1 visit in 2 yrs 18% seen just 1 time
Reviewed 60 charts for each screening using CSLC report Found no discrepancy with Pap Smear rate Found 2 mammograms not reported in report
CSLC Case Study WCHC - 04/25/07 30
Lessons Learned
Get it right from the start! Do whatever it takes to make sure the data goes into the system correctly.
Data validation and maintaining data integrity is a constant process, not just a one-time project.
Make sure someone owns the responsibility for data stewardship, supported by the leaders of the organization.
It’s critical to understand the sources of information for each data element, and to conduct controlled testing.
CSLC Case Study WCHC - 04/25/07 31
Summary of STRIVE in Action
Standards have been set for Superbill processing, All
Staff involved in DATA COLLECTION/ USE
Clear responsibility for data stewardship and management supported by ED;
Incentives toward data integrity- Data graphs for feedback to staff and Providers
Validation-Using two sources of information (CSLC Report and i2iTracks Roster) plus chart review
Education- Ongoing
Training provided to improve accuracy of coding, all data entry, and data validation steps