The challenges of capturing clinically accurate data for electronic - - PowerPoint PPT Presentation

Joe Kunisch PhD, RN-BC, CPHQ Enterprise Director of Clinical Quality Informatics Memorial Hermann Hospital System Quality Patient Safety & Infection Control

The challenges of capturing clinically accurate data for electronic Clinical Quality Measures (eCQM)

• Data must be discrete- able to be identified by a distinct

computer code

• Data is constrained to value sets- a value set determines

what text-based terms are included

• Data capture can be limited to specific fields- i.e. a

check box in a single EHR form

• Data may be required to be captured at certain points in

time or workflow- i.e. in the first 24 hours of admission

• Data may reside in disparate information systems- i.e.

partly in an EHR and partly in a diagnostic system

Current Challenges of Capturing eCQMs

Volume of eCQM Cases Across 9 Hospitals

Physicians & Nurses must complete documentation 100%

• f the time on 100% of the patients in the same method

electronic Clinical Quality Measure (eCQM) Sets 2015 Total eCQM Abstraction (100%) Emergency Department Patients 503,340 Patients Venous Thromboembolism Patients 104,892 Patients Perinatal Quality Measures 52,667 Patients

eCQM Example VTE-6

• Incidence of Potentially-Preventable Venous

Thromboembolism

• This measure assesses the number of patients diagnosed

with confirmed VTE during hospitalization (not present at admission) who did not receive VTE prophylaxis between hospital admission and the day before the VTE diagnostic testing order date.

• Failure to prevent VTE can result in delayed hospital

discharge or readmission, increased risk for long-term morbidity from post-thrombotic syndrome, and recurrent thrombosis in the future.

eCQM Example VTE-6

Case Scenario: Patient is brought into the hospital emergency room after suffering a fall at home. Patient also has slurred speech, is confused and notable swelling to the left leg. Head and Pelvis CT Scan confirms left subarachnoid hemorrhage and right fractured hip. D-Dimer lab result is elevated. Patient is admitted to the Neuro ICU, orders include ultrasound study to the left lower extremity to rule out DVT but the patient deteriorates and the test it delayed until day 3 of inpatient stay.

eCQM Example VTE-6

Discrete, coded and defined appropriately

Initial Population: Patients age 18 and older discharged from hospital inpatient acute care with a non-principal diagnosis of venous thromboembolism (VTE).

• The principal diagnosis is defined in the Uniform Hospital

Discharge Data Set (UHDDS) as 'that condition established after study to be chiefly responsible for occasioning the admission of the patient to the hospital for care.‘

• Physician documents in text note confirmed SAH, right hip fracture

and possible LLE embolism because of the swelling and elevated D- Dimer.

• Which one is the principal diagnosis?
• What was present on admission (POA)?

Data may reside in disparate information systems

• Denominator = "Initial Patient Population"
• ◦AND: "Occurrence A of Diagnostic Study, Order:

VTE Diagnostic Test" starts during "Occurrence A of Encounter, Performed: Encounter Inpatient"

• AND: First: "Occurrence A of Diagnostic Study, Performed:

VTE Diagnostic Test (result: VTE Confirmed)" starts during Occurrence A of Encounter Inpatient

• Ultrasound test was ordered at admit but was not

performed until day 3 of inpatient status.

• Are all the data elements recorded in the EHR or a common

database?

• If the test results are captured in a separate information

system, how is recorded in the EHR?

• Is it discrete? Is it text based? Does documenting it in a

discrete data field within the EHR require a new workflow?

• Who is responsible for documenting a positive finding in a

newly created EHR form created to capture the discrete data?

Data may reside in disparate information systems

Data constrained by value sets and limited to time span Denominator Exclusions =

• "Diagnosis, Active: Venous Thromboembolism" AND

Emergency Department Visit" <= 1 hour(s) ends before Inpatient Encounter, OR: <= 1 day(s) starts after Inpatient Encounter starts

• Specific time frame for when a physician enters a

diagnosis determines if the patient is excluded or not

• "Diagnosis, Active: VTE" using "VTE SNOMEDCT Value

Set (2.16.840.1.113883.3.117.1.7.1.279)"

Data constrained by value sets

• Involve clinicians in the conceptual phase of any

measure development

• Understand how the data is normally captured in the

workflow

• Understand how the data is used and it’s importance in

the clinical domain

• Understand the impact that data mapping may have on

the success of capturing clinically accurate data

• Success= The ability to extract existing data without a

change in the EHR interface or the clinical workflow.

Recommendations to increase clinical data accuracy