# Presentation: 337900

## Automating Quality Reporting with Electronic Health Records

- Kristine Martin Anderson, Principal
- Washington, DC
- October 11, 2006

**DISCUSSION DOCUMENT**

**Automating Quality Reporting with Electronic Health Records**

*This document is confidential and is intended solely for the use and information of the client to whom it is addressed.*

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## Collecting accurate quality data, however remains a resource intense and complex process

- Chart review
- Clinical staff
- Significant quality training
- Labor intensive and time consuming

- 20 measures for CMS today, more to be added
- Private payors request data independently
- Increased requirements to support P4P and consumer driven health

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## Policy makers, hospital leaders and regulators envision a future where care can be automatically evaluated using Electronic Health Records

**Policy makers, hospital leaders and regulators envision a future where care can be automatically evaluated using Electronic Health Records**

**Measure - The percent of acute myocardial patients who have received a beta blocker within 24 hours of arrival at the hospital**

**Automation Scenario**

- The nurse administers the medication and documents the event in an electronic medication administration record (e-MAR).

- The hospital has an established standing order for all patients identified as heart attack (AMI) patients.  The order set prompts physicians to order oxygen, aspirin, a beta blocker and other therapeutic interventions.

- The electronic health record transmits the order for the beta blocker to the pharmacy, where the order is verified, filled and the drug is dispensed.

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## The primary challenge for automating process measures is accurately identifying which patients are eligible for the intervention

_**Contra-Indications**_

**Beta blocker allergy**
**Bradycardia (heart rate less than 60 bpm) on day of arrival or day prior to arrival while not on a beta blocker**
**Second or third degree heart block on ECG on arrival or within 24 hours after arrival and does not have pacemaker**
**Heart Failure on arrival or within 24 hours after arrival**
**Shock on arrival or within 24 hours after arrival**
**Other reasons documented by a physician, nurse practitioner or physician assistant for not prescribing a beta blocker**

_**Medical Record Data Sources**_

**Measure - The percent of acute myocardial patients who have received a beta blocker within 24 hours of arrival at the hospital**

-  **Consultation notes**
** ****Discharge summary**
** ****ECG reports**
** ****Emergency department record**
** ****History and physical**
** ****Medication administration record**
** ****Nursing notes**
** ****Physician orders**
** ****Progress notes**
** ****Vital signs graphic record**

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**Notes:**
The point is that there are a variety of clinical notes that have diagnosis information (e.g., H and Ps, progress notes, nursing notes, consultation notes, emergency department record, physician orders, discharge summaries, orders, problem lists, etc.) and a number of places in notes that this will occur (i.e. problem lists, assessments, chief compliant, history of present illness, etc). By repeating "clinical notes," this looks repetitive.  

Once could say "multiple places in various clinical documentation" and footnote the places and note types I've indicated in above.  

I'd consider combining 4 and 5 which are related both to poor heart pump function.  In fact, one could combine 4-6 into one category. 

## There are very few standards for clinical documentation, making analysis across charts difficult and across hospitals impossible

- Documentation can occur in many places in the medical record, complicating search algorithms and confusing the results
- Clinical documentation is often unstructured and uses non-standardized nomenclature
- Clinical documentation is often the last module implemented by hospitals, as it requires significant change management for clinicians, who are often already feeling burdened with CPOE
- There is insufficient active and passive encouragement of documentation that would automate quality measurement

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## Quality Measurement relies on linkages between ambulatory and inpatient records, which often do not exist

- Data that indicate some contraindications would be present in ambulatory records
- Example: For patients admitted with an AMI is important to urgently discover allergies, history of pulmonary disease, history of diabetes or hypoglycemia, etc.
- Ambulatory records need to be accessible quickly to ensure compliance with time-based standards of care.
- Example: A patient with chest pain gets an EKG in an ambulance. In order to meet door to balloon requirements, the EKG must make it to a cardiologist who can be expected to intervene.  The transfer of waveform data to an EMR is a critical need but unmet in many healthcare entities because waveform storage and dissemination is outside of the traditional perception of EMR's.

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## We cannot automate quality reporting simply by increasing adoption of the current generation of electronic health records

- Electronic Health Record Adoption

- Electronic Health Record Evolution to Support Quality

- 2004

- 2006

- 2008

- 2010

- 2012

- 2014

*time*

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**Notes:**

to Support Quality Measurement, Decision Support, Bio-Surveillance, Clinical Research

## The common barriers to automation must be addressed in order to improve quality of care and streamline reporting

| Issue | Recommendation | Considerations |
| --- | --- | --- |
| Quality specifications are insufficient for IT coding | Create a template for measure specification driven by NQF with input from measure developers and IT vendors | Specifications should go beyond quality metrics to more broadly cover evidence based guidelines for decision support |
| Free text clinical documentation hampers reporting
 | Promote the use of focused documentation templates that are integrated into workflow with CDS that improve provider efficiency and patient safety | Cultural barriers to template use
 |
| Varying nomenclature complicates queries
 | Support certification initiatives that include data storage standards specific for quality measurement | Data mapping tools will be critical in areas where standards are difficult to implement |
| EHR architectures are primarily designed to support care and do not support population analysis | Promote adoption of automated EHR data stores designed for analytics | Warehousing solutions and strategies can be expensive |
| Clinical documentation is frequently one of the last EHR modules to be implemented | Create hospital playbooks to inform how to implement EHR’s that support measurement | National dialogue on automation of quality measure may be needed to drive adoption |

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