Patients Government Providers Payers Quality · Managing Clinical Knowledge for Health care Improvement, ... Successful Change Management . Knoster, T., Villa R., & Thousand, J

Quality

Patients

Providers

Implementers Measure Developers

Government Payers

Capturing EHR Enabled Quality Improvement- Decision Support, Measurement, and Reporting REQUIRES an

understanding of context February 23, 2014

John Chuo, MD, MS Children’s Hospital of Philadelphia

Neonatal Quality Officer Medical director, Telemedicine

DISCLAIMER: The v iews and opinions expressed in this presentation are those of the author and do not necessarily represent of f icial policy or position of HIMSS.

Recognizing a bad care delivery system does not mean you know how to

improve it.

CQM measures how good or bad healthcare delivery

is

but

Poor understanding of system = Poor implementation of best practices

Managing Clinical Knowledge for Health care Improvement, EA Balas, 2000

Using clinical quality measures to drive improvement in

healthcare delivery requires

understanding of context.

Use of clinical decision support system

Principles of best practice for CDS implementation

Evidence, expert, experience

Compliance with Using CDS intervention

Implementation Outcome

CQM Outcome

(Prophylactic antibiotic received within 1 hour prior to surgery)

CDS as a means for improving CQM

Can it work?

(Efficacy)

Does it work In MY

context? (Effectivenes

s)

© John Chuo, manuscript in progress

Successful Change Management

Knoster, T., Villa R., & Thousand, J. (2000) A framework for thinking about systems change.

Is the desired patient

outcome clear?

Does user have the

skills to use the CDS? (training)

Why would the user use the

CDS, why not override?

Do you have resources to react

to feedback Promptly

What is the action plan for improving the

CDS?

FOR CDS IMPLEMENTATION

EXAMPLE:

Implementing a paper reminder system using a CDS backend on patient rounds in the NICU

Admin

Medical Team

Computer system

1. Runs the rounding tool

system each am

QI Student intern

2. Print out QI reminders depending on patient conditions (by searching thru previous day notes)

3. Delivers the paper QI tool with the reminders to each of the rounding teams

Conducts patient rounds

4. Student ask reminders during rounds and record

answers

5. Updates data repository

Rounding tool workflow

Rounding tool

Results

% of time assigned questions were

applicable to patient

% of times assigned question needed

prompting by intern

% times assigned question had

Response unfavorable to patient

(Importance factor)

% of time assigned question not asked, Unfavorable and an

Action Was Prompted (Impact factor)

AVG. 93% 11% 10% 6% (600 actions)

0.00%

20.00%

40.00%

60.00%

80.00%"Third Trimester HIVResults" (7/11)

"Social History" (3/5)

"Family History" (1/2)

"O2 Sat. Limits" (1/7)

"Immunizations" (1/8)

Top 5 questions that needed prompting by intern, response

unfavorable to patient, prompted Action

© John Chuo, manuscript in progress

John Chuo [email protected]

Contact Info

12

mailto:[email protected]

Conflict of Interest Disclosure

John Chuo, MD, MS

• No conflicts of interest to disclose

© 2014 HIMSS

Capturing EHR Enabled Quality Improvement- Decision Support, Measurement, and Reporting

February 23, 2014 Floyd Eisenberg, MD, MPH, FACP

iParsimony, LLC The presentation includes content from the American Hospital Association. A Study of the Impact of Meaningful Use Clinical Quality Measures. July 2013.


© 2013 American Hospital Association

eCQM implementation process – Eligible Hospitals

Common implementation steps observed at all sites Iterative, non-linear process

Gap analysis

Data capture and workflow

redesign

Data extraction and eCQM calculation

Validation Downstream

uses of eCQM results

American Hospital Association. A Study of the Impact of Meaningful Use Clinical Quality Measures. July 2013. Available at: http://www.aha.org/content/13/13ehrchallenges-report.pdf.

http://www.aha.org/content/13/13ehrchallenges-report.pdf

eCQM implementation experience – Eligible Hospitals

Gap analysis

Data capture and workflow

redesign

Data extraction and eCQM calculation

Validation Downstream

uses of eCQM results

Largely prescribed by EHR/eCQM reporting tool

Workarounds and successive iterations • 80% of effort entailed changes to hospital workflow

solely to accommodate eCQM data capture

Innacurate eCQM results • Sensitivity issues causing under reporting:

• Data not in prescribed location (as expected by eCQM tool) • Internal systems with needed data not interoperable with EHR

• Usability affecting data quality

No trust or reliance in eCQM results



Program Challenges

1. eCQMs were not tested for validity, accuracy and feasibility

2. eCQMs were hard to find, lengthy, and often contained errors

3. MU eCQMs require understanding of unfamiliar terminologies

4. Guidance to ignore data accuracy and focus on the ability to report undermines goals for quality improvement

eCQM Impact Study: Challenges



Technical Challenges

Expectations that existing EHR data would suffice to calculate the eCQMs were not realized

1. EHRs do not store entered data in readily retrievable form

2. EHRs are not designed to capture many of the elements in structured form to enable re-use for eCQM reporting

3. EHRs are not designed to capture information from other department information systems at the level of detail needed for eCQM reporting




Clinical Challenges

1. EHRs and certification requirements are not designed to support end-to-end patient care workflows to draw data as expressed in eCQMs

2. Hospitals were unable to validate the eCQM results

3. Some eCQM specifications were out of date

Strategic Challenges

1. Time and personnel requirements to implement eCQMs were excessive and far beyond expectations

2. The time and effort provided no return on investment as results could not be validated and were therefore not useful for quality management.




eCQM Impact Study: Recommendations





Updated Process: A World of Dependencies

Standard Vocabulary

Quality Data

Model

Measure Authorin

g Tool

Value Set Authority

Center

Health Quality Measure Format

Quality Report Document

Architecture

Clinical Document

Architecture

Review Oversight - HHS


Courtesy: The Joint Commission


eCQM to CDS Example: Measure CMS 165 (NQF 0018)

CONDITION: All are true: Patient is 18 - 84 years Patient has diagnosis = Hypertension [“Active”] Patient does not have diagnosis = Pregnancy Patient does not have diagnosis = End Stage Renal Disease Patient has not had procedure during the measurement year = ESRD-related procedures

APPLIES WHEN: Diastolic Blood Pressure > 90 OR Systolic Blood Pressure > 140 during the last visit Advice: Provide list of patients with possible need for follow up AND hyperlink to NHLBI Guidelines http://www.nhlbi.nih.gov/guidelines/hypertension/index.htm And provide patient education resources: http://www.nhlbi.nih.gov/health/health-topics/topics/hbp/

APPLIES WHEN: Diastolic Blood Pressure OR Systolic Blood Pressure are absent during the last visit Advice: Provide list of patients with indication blood pressure should be taken at each visit AND hyperlink to NHLBI Guidelines http://www.nhlbi.nih.gov/guidelines/hypertension/index.htm And provide patient education resources: http://www.nhlbi.nih.gov/health/health-topics/topics/hbp/

Trigger

Condition

Actions

New data: Query Patient Registry q30 Days

Evaluate: 1. Currency of evidence 2. Feasibility of elements in clinical workflow 3. Value set content

http://www.nhlbi.nih.gov/guidelines/hypertension/index.htm


http://www.nhlbi.nih.gov/health/health-topics/topics/hbp/






Floyd Eisenberg [email protected]

Contact Info

23

This presentation includes content from the American Hospital Association. A Study of the Impact of Meaningful Use Clinical Quality Measures. July 2013.

© 2013 American Hospital Association



Floyd Eisenberg, MD, MPH, FACP


© 2014 HIMSS

Capturing EHR Enabled Quality Improvement- Decision Support, Measurement, and Reporting

February 23, 2014

Ferdinand Velasco, M.D., FHIMSS Chief Health Information Officer, Texas Health Resources

Chair, HIMSS Quality Cost Safety Committee DISCLAIMER: The v iews and opinions expressed in this presentation are those of the author and do not necessarily represent of f icial policy or position of HIMSS.

One of the largest faith-based, nonprofit health care delivery systems in the United States and the largest in North Texas in terms of patients

served. The system's primary service area consists of 16 counties in north central Texas, home to more than 6.2 million people.

www.texashealth.org

Organizational Background

26

Texas Health Resources

• EHR highlights – 2013 Davies Award – All hospitals at HIMSS EMRAM Stage 6 or 7 – Attested to Meaningful Use Stage 1 (3 years)

We are here…

• Received 80% of anticipated EHR incentive funding from 3 years of Stage 1 MU

• Addressing challenges of meeting Stage 2 MU objectives

• Utilizing historical methods for HIQR and PQRS reporting

• Shifting organizational focus from acute care to population health management

Case studies

• Purpose: to illustrate the considerations of transitioning from Chart Abstracted Measures to eMeasures

• ED throughput: median time from ED arrival to departure for admitted patients

• Ischemic Stroke: anticoagulation therapy for atrial fibrillation/flutter

ED throughput (NQF 0495)

Source: CMS, http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/2014_CQM_EH_FinalRule.pdf


• Measure output relevant and meaningful both internally and externally

• eMeasure specification relative simple

• Documentation of workflows able to capture discrete EHR

• Excellent correlation between manual abstraction and EHR method


0

50

100

150

200

250

300

350

ED-1.1

ED-1.1


• Measure output relevant and meaningful both internally and externally

• eMeasure specification relative simple

• Documentation of workflows able to capture discrete EHR

• Excellent correlation between manual abstraction and EHR method

Potential opportunities for improvement • More meaningful segmentation • Correlation with

– ED / hospital census – ED wait times – ED staffing ratios – Syndromic surveillance – Patient satisfaction

• Use of realtime location sensing technology to eliminate manual time stamps in EHR

• Consider similar measures for inpatient, OR, ambulatory throughput

Source: Texas Dept. of State Health Services, Texas Influenza Surveillance Report 2013–2014 Season

Stroke: anticoagulation therapy for atrial fibrillation/flutter (NQF 0436)

Source: CMS, http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/2014_CQM_EH_FinalRule.pdf

Stroke: anticoagulation therapy for atrial fibrillation/flutter (NQF 0436) • Measure output relevant and

meaningful both internally and externally

• Measure logic complex

• Documentation of workflows uneven

• Challenges with translating EHR data into discrete variables needed to generate CQM

• Modest success with reconciling abstracted and EHR-derived data

Source: AHRQ United States Health Information Knowledgebase, http://ushik.org/mdr/portals

Stroke: anticoagulation therapy for atrial fibrillation/flutter (NQF 0436)

Source: AHRQ United States Health Information Knowledgebase, http://ushik.org/mdr/portals

Stroke: anticoagulation therapy for atrial fibrillation/flutter (NQF 0436) • Measure output relevant and

meaningful both internally and externally

• Measure logic complex

• Documentation of workflows uneven

• Challenges with translating EHR data into discrete variables needed to generate CQM

• Modest success with reconciling abstracted and EHR-derived data

Potential opportunities for improvement • Reduce measure logic complexity

– Fewer exclusions – Be more parsimonious and

prescriptive about definitive data sources

• Correlation with – Patient education – Use of secure messaging – Long term anticoagulation

effectiveness and safety • Possible CDS application

– Checklist (pre-discharge)

Lessons learned / considerations

• Process – Build CQM logic manually from measure specifications OR… – Utilize eMeasure specifications from certified EHR technology provider – Validation of CQMs

• Prioritization: CQM reporting for… – Internal process improvement – External reporting

• Pay for performance • Pay for reporting

• Measure overlap vs. separation – EH: HIQR / VBP / MU – EP: PQRS / ACO / MU

Conclusions

• Transition from manually abstracted measures to eMeasures will be a long journey

– Approach needs to be meticulous and systematic – Some eMeasures are usable; most are not

• Abstraction will likely never be completely eliminated, at least for CQMs with complex measure logic

– Shift focus from retrospective chart abstraction to concurrent care management

Ferdinand Velasco [email protected]

HIMSS Quality Cost Safety Committee http://www.himss.org/get-

involved/committees/quality-cost-and-safety

Contact Info

44



Ferdinand Velasco, MD


© 2014 HIMSS

Integrating Quality Measurement and CDS-enabled Quality Improvement

February 23, 2014 Jerome A. Osheroff, MD, FACP, FACMI

Principal, TMIT Consulting, LLC Adjunct Associate Professor of Medicine, University of Pennsylvania


We are Here… • Strong/mounting pressure for measurable improvements

• Sub-optimal data to understand care process/outcomes

• Difficulty enhancing measurement/performance

QM & CDS worlds both working on these Turn challenges to joint opportunities!

CDS Definition

“A process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve health and healthcare delivery.”

Improving outcomes with CDS, 2nd Ed. HIMSS 2012

Reinforces inter-dependence of QM and QI

QI Success Framework: CDS Five Rights

To improve targeted care processes/outcomes, get:

• the right information evidence-based, actionable… [what]

• to the right people clinicians and patients… [who]

• in the right formats documentation tools, data display, answers, order sets, alerts… [how]

• through the right channels EHR, portals, smartphones, smart pill bottles/monitors… [where]

• at the the right times key decision/action … [when]

49

ONC Toolkit: Resources for Improving Care with CDS*

50

*Posted at: bit.ly/CDS4MU

https://sites.google.com/site/cdsforpiimperativespublic/CDSQI-resources

Getting Better Faster – Together(SM): A Case Example

Warm-up Questions (To Engage Site Leads)

1. What % of our HTN patients have BP<140/90?

2. How are we supporting patient and clinician decisions and actions to drive improvement?

3. Are we using our EHRs (and other tools) to greatest benefit for workflow and outcomes?

4. How can we work as a group to get more efficient at Quality Improvement (QI) and Collaboration?

52

Improving BP Control

53

• What needs to happen? ‒ Decisions ‒ Actions ‒ Communication ‒ Data gathering

• In RCH health centers, today ‒ What information, ‒ Flows through which people, ‒ In what formats/channels, ‒ At which times?

Ambulatory Worksheet (Simplified)

54

TMIT Consulting, LLC

Patient List/Registry: Powerful Non-Alert CDS Tools

55

From Dr. Chris Tashjian, Ellsworth Medical Clinic, with permission

• Document target-focused information flow – Invariably suggests potential enhancements – Helps get QI team/practice ‘on the same page’

• Foster collaboration

– Provider <-> Provider (via Collaborative private site) – Vendor <-> Provider client – QI Experts (Million Hearts) <-> Implementers/Vendors

QI/CDS Worksheets: Tool to Get “CDS 5 Rights” Right

56

Measure Developers <-> Others?!

Steps for CDS-enabled QI*

1. Shared understanding of CDS/QI concepts 2. Select improvement targets 3. Envision successful target-focused CDS/QI 4. Use CDS/QI worksheets 5. Make enhancements (“Plan-Do-Study-Adjust”) 6. Collaborate

*See bit.ly/CDSQISteps

Role for QM Community?!

Jerry Osheroff [email protected]

ONC CDS for MU/QI Tools and Resources bit.ly/CDS4MU

CDS/PI Collaborative (Public) bit.ly/CDSPICollab

Contact Info

58


https://sites.google.com/site/cdsforpiimperativespublic/CDSQI-resources

http://www.bit.ly/CDSPICollab

Additional slide follows:

Inpatient example of CDS/QM interplay: Clinical intervention contraindications (and CQM measure exclusion) captured during care delivery in a ‘smart order set’: simultaneously serves QI/CDS/QM purposes

An Inpatient Example: VTE Prophylaxis • Best practice CDS/QI recommendations • Based on Society of Hospital Medicine expertise • Anchored by order sets that provide:

– Risk stratification (Hi/Med/Low) – Orders pertinent to risk strata – Opportunity to document contraindications – Uses unit dashboards analogous to registry

Simultaneously support best care, measurement, reporting

For recommendations see: https://sites.google.com/site/cdsforpiimperativespublic/projects/vte-best-practices


Jerome A. Osheroff, MD

• Ownership Interest – TMIT Consulting, LLC provides healthcare quality

improvement-related professional services, and helped developed the freely available ONC CDS/QI resources mentioned in this talk.

© 2014 HIMSS

Documents

Patients Government Providers Payers Quality · Managing Clinical Knowledge for Health care Improvement, ... Successful Change Management . Knoster, T., Villa R., & Thousand, J