Rush University Medical Center Meaningful Use Case Study

IS 574-701 Business Intelligence

Meaningful Use and EHR Systems

Final IS-574-701 Hoppe - MEANINGFUL USE AND EHR SYSTEMS

Table of Contents
Project Introduction 2
Clinical Research 7
Care Delivery Organizations (CDO) 9
Rush University Action Plan 11
Process to Evaluate Clinical Metrics 15
Rush University Conclusion 16
Metric Sources 16
Conclusion 21
References 22

Project Introduction
The Healthcare industry, due to President Obama’s enthusiastic endorsement, is in the

midst of converting its current patient healthcare records system over to an Electronic Health

Record (EHR) or Electronic Medical Record (EHR) system. An EHR is “a longitudinal

electronic record of patient health information generated by one or more encounters in any care

delivery setting and includes information about patient demographics, diagnosis, treatments,

progress notes, problems, medications, vital signs, past medical history, immunizations,

laboratory data and radiology reports. When properly aligned with the definition of Meaningful

Use, EHR provides ways of collecting, analyzing and presenting relevant patient data about

patient demographics, diagnosis, treatments, progress notes, problems, medications, vital signs,

past medical history, immunizations, laboratory data and radiology reports. These statistics

shows a steady increase in the percentage of office-based physicians with some form of EHR

systems in use. Although there is steady progress of acceptance, change agents must get the

word out to educate and inspire other physicians to understand the benefits or EHR. Blogs and

LinkedIn group’s discussions might be a few ways of spreading the word.

2009 survey data (mail survey and in-person survey) concluded that 48.3% of physicians

reported using all or partial EMR/EHR systems in their office-based practices. Nearly 21.8% of

physicians reported having systems that met the criteria of a basic system, and about 6.9% reported

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having systems that met the criteria of a fully functional system, a subset of a basic system.

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Preliminary mail survey 2010 estimates showed that 50.7% of physicians reported using all

or partial EMR/EHR systems, similar to the 2009 estimate. About 24.9% reported having systems

that met the criteria of a basic system, and 10.1% reported having systems that met the criteria of a

fully functional system, a subset of a basic system.

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Between 2009 and 2010, the percentage of physicians reporting having systems that met the

criteria of a basic or a fully functional system increased by 14.2% and 46.4%, respectively10.

Reluctance in getting a fully functional system may result in either trying to limit financial

investments due to uncertainty of continued acceptance or not fully realizing the benefits of EHR

systems.

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Understanding hospital’s levels of electronic medical record (EHR) capabilities is a

challenge in US Healthcare IT. Healthcare Information and Management Systems Society

(HIMSS) AnalyticsTM has created an EHR Adoption Model that classifies the electronic

medical record (EHR) capability levels, ranging from limited ancillary department systems

through a paperless EHR environment. HIMSS Analytics developed a methodology and

algorithms to automatically score more than 5,000 U.S. and nearly 700 Canadian hospitals

relative to their IT-enabled clinical transformation status, to provide peer comparisons for

hospital organizations as they strategize their way to a complete EHR and participation in an

electronic health record (EHR). The stages of the model include:

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Detailed explanation of EHR Adoption Stages

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HIMSS Analytics has tracked the progress of U.S. hospitals as they successfully progress

through the eight stages (Stages 0-7) of the EHR Adoption Model. The chart below exhibits the

progress of each stage from 2008 to current day (3rd quarter 2011). Each stage shows steady

progress with Stage 3 displaying the highest percentage of achievement and Stage 7 showing the

lowest. The themes of Stages 0 through 3 focus on hospital participation in general with Stage 3

highlighting nursing and clinical documentation. Stages 4 through 7 exhibits more clinician and

medication reconciliation involvement with the eventuality of full physician documentation for

Stage 6 and paperless records in Stage 7. The trend seems to indicate the more EHR

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involvement required the less interest exhibited by required individuals due to time, money and

educational investments.

U.S. Hospital EHR Adoption Stage Progress Q4 2008 – Q3 2011

Business Intelligence, examining data visually, can provide stimulating means of

presenting comprehensible data while helping to connect with the definition of Meaningful Use.

The concept of “Meaningful Use” rests on the ‘5 pillars’ of health outcomes policy priorities,

Improving quality, safety, efficiency, and reducing health disparities
Engage patients and families in their health
Improve care coordination
Improve population and public health

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Ensure adequate privacy and security protection for personal health information1 The

American Recovery and Reinvestment Act (ARRA) lays out the three main components

of Meaningful Use definition and achievement.

The ARRA provides three main components of Meaningful Use:
- The use of a certified EHR in a meaningful manner, such as e-scribing.
- The use of certified EHR technology for electronic exchange of health
information to improve quality of health care.
- The use of certified EHR technology to submit clinical quality and other
measures2.

Clinical Research
Through EHR data collection and storage, there is a potential to provide Meaningful Use

while enhancing the clinical research process in hospital settings by applying a Business

Intelligence (BI) framework to create Clinical Research Intelligence (CLRI) frameworks for

optimizing data collection and analytics3. As the healthcare industry undergoes this paradigm

shift, clinical researchers confront opportunities and challenges to acquire knowledge using a BI

approach to recruit stakeholders, collect and analyze data with prospects of hypotheses

generation.

The use of clinical decision support, with EHR rules and alerts, can alert physicians of

patient’s clinical trials eligibility. While engaged in a patient encounter, if the patient satisfies

the clinical trial criteria, the physician receives a potential clinical trial candidate alert. One

study found using the EHR clinical trials alerts significantly increased the number of physicians

participating in clinical trial recruitment process while minimizing referral bias and extending

recruitment to a wider patient population. Furthermore, there is more productive EHR based

hypotheses creation and research-study completion potential performed electronically rather than

the once tedious and manual method.

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Business Intelligence (BI) is defined as “the process of turning data into information and

then into knowledge3” and is used for organizational decision support and performance

management. BI’s main objective is providing users with interactive data access and providing

business managers and analysts the ability to conduct analysis. In the same fashion, clinical

researchers abstract or collect data through database reporting or patient record review and use

historical and current data, situations and outcomes to determine potential issues and generate

hypotheses and support studies. Normal transactions occur within normal workflow processes

such as patient registration, transcribed or structured reporting, computerized physician order

entry (CPOE) and clinical documentation.

Business Analytics is a term used to for the tools and techniques used to gather and

analyze data for business and strategic decisions3. Additionally, Business Analytics helps in

gathering, collecting and storing clinical research data, and categorized under clinical research

intelligence (CLRI). Business Analytics has three categories information and knowledge

discovery; decision support and intelligent systems and visualization and they contribute to the

framework for clinical research data needs. Information and Knowledge Discovery uses OLAP

(on-line analytical processing,) ad hoc queries, data mining, text mining, web mining, and search

engines, which are useful and relevant to the clinical research data collection and analysis

process. Decision Support and Intelligent systems includes statistical analysis, data mining and

predictive analysis used in hypothesis generation as well as data collection, analysis supporting

research goals and initiatives. Finally, clinical researchers may utilize visualization, scorecards

and dashboards in clinical trial progress reports and trending patient outcomes. With their

proven value to any organization, clinical business intelligence capabilities is expected to be

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included in EHR offerings as required by the American Recovery and Reinvestment Act of 2009.

The table below highlights to four values that clinical business intelligence provides:

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Value of Clinical Business Intelligence in the Delivery of Patient Care
Integration – merges clinical and financial data to Risk Mitigation – perform data analysis to see into
allow providers to make more informed decisions the future and be productive in order to avoid risks
Performance management – tracks and measures Collaboration – enhances the ability of providers
clinical performance and how it directly impacts to work together to monitor the progress of patients
patient outcomes

Care Delivery
Organizations (CDO)
Inside of Care Delivery Organizations (CDO), utilization reduction, nursing

administrative time, inpatient drug usage and outpatient drug and radiology usage activities

projected the most significant efficiency gains. Computerized order entry alerts and reminders,

that reduce adverse drug events, provide necessary safety benefits. Nurses play an important role

as designers and users of electronic documentation system, since they are the largest consumers

of HIT and use data from the electronic health record to tell the patient story. Priorities form

nursing executive include streamlining documentation, optimizing workflows, accessing

Meaningful Use data, nursing alerts to promote evidence based practice and matching hardware

investments to desired timeliness of documentation.

Sometimes the only way to see Meaningful Use in action is to review a real

implementation and measure the results and one such implementation occurred at Rush

University Medical Center in Chicago. Rush University Medical Center (Rush) is an academic

medical center which includes a 671-bed acute care hospital serving adults and children, a 61-

bed Johnston R. Bowman Health Center and Rush University. Rush launched a 10-year project

starting in 2006, titled the Rush Transformation, to build new facilities and implement an

integrated EHR system because of the recognition that such a system would significantly

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improve quality of care. Rush broke the project down into the following phases and timelines

focusing on certain modules during each phase.

Rush University Implementation Phases

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Rush University
Action Plan

Rush University identified clinical metrics to aid in evaluating electronic health record

quality improvement elements.

Rush University Phase 1 Clinical Metrics

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The project’s goal zeroed in on creating processes for ongoing data collection evaluation,

analysis and clinical metrics reporting. The end data will provide evidence of the impact of the

commercially developed electronic health record on the quality of patient care. Meyer5 identifies

four basic steps for creating process measures:

1. Define critical factors
2. Map cross-functional processes
3. Identify critical tasks
4. Design measures to track critical factors.

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Rush leaders wanted to focus their process measures on Process Improvement, Patient

Experience, Quality Outcomes and Fiscal Responsibility and within this strategic framework,

and included these critical factors for EHR clinical aspects:

1. Medication Reconciliation
2. Nursing Assessment
3. Diagnosis Documentation
4. Discharge Instructions
5. Clinician Satisfaction
6. Timely Care Delivery
7. Timely Documentation
8. Screening for prior to admission conditions
9. Patient Satisfaction.
For pilot testing, Rush University leaders recognized the following three critical factors:

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After implementing the EHR system, partial medication reconciliation increased from

88.9 percent to 94.6 percent while sustaining improvements for the entire study period. Full

medication reconciliation increased from 75.1 percent to 80.2 percent7, with unsustainable

improvements and all results fell within a 95-percent confidence interval. The task force

identified three factors that contributed to incomplete medication reconciliation: patients do not

have complete information on home medications; clinicians enter duplicate medications when

using both brand and generic names; and clinicians lack full knowledge of designed workflows.

Medication Reconciliation2
• The Joint Commission on Accreditation of Healthcare Organizations (JCAHO)
National Patient Safety Goal 8: Accurately and completely reconcile medications
across the continuum of care

• JCAHO identifies goal 8’s five steps:
1) develop a list of current medications
2) develop a list of medications to be prescribed
3) compare the medications on the two lists
4) make clinical decisions based on the comparison
5) communicate the new list to appropriate caregivers and to the patient.

Electronic health records medication list studies discovered data is only accurate if entered

correctly. Data entry errors account for 28 percent of the discrepancies, while clinician’s failure to

enter medication changes into the electronic record account for 26 percent. This demonstrates that

standardized medication reconciliation process implementation reduces the number of unintended

patient discrepancies by 43 percent, thereby significantly decreasing the potential for medication

errors. Use of an EHR order entry system can reduce errors at the time of discharge by generating a

list of medications used before and during the hospital admission and can be printed and used for

education and patient review. This system’s usefulness depends upon the prior implementation of an

admission medication reconciliation system.

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Some electronic discharge medication ordering systems allow for direct transfer of the orders

to the community pharmacy and to the primary care physician, as well as keeping a permanent record

on the electronic health record. Electronic systems make it easier to access medication histories, with

frequent updates and information correlation with patients’ actual medication use. Electronic

prescribing also allows for decision support such as checking for allergies, double prescribing and

counteracting medications.

The Joint Commission’s standard for discharge instructions requires clear documentation

that the patient/caregiver received a copy of the written instructions; including discharge

medication list at discharge. Due to this Joint Commission’s requirement and the impact that

discharge instructions have on safe medication practices, Rush leaders identified discharge

instructions as a high priority clinical metric. Survey results analysis found that patient

satisfaction with discharge instructions did not significantly change after EHR implementation.

Key nursing operations stakeholders speculated that premature evaluation of this metric occurred

as it is a new, complicated process and required a longer learning curve. Expectations focused

on the eventuality of clinicians becoming more comfortable with the discharge process, leading

to increased patient satisfaction.

In order to comply with the medication reconciliation process, Rush University adopted

the process created by Dr. Spath7, which outlines the seven-step outcomes management process:

Define objectives
Identify performance measures
Select measurement tools
Define measurement methods
Collect data
Transform data into information (data analysis)
Use the information to improve performance

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Three additional steps help to evaluate the EHR metrics process:

Determine data significance
Create report templates
Identify gaps between designed workflows and actual practice.

Process to Evaluate
Clinical Metrics

The Rush University EHR metrics project objective evaluated the EHR impact on high

quality and safe care’s closely related critical tasks by developing an evaluation process and

piloting this process with the following three metrics:

Medication reconciliation
o Patients will have complete medication reconciliation at discharge.
Problem list documentation
o Patients will have at least one current problem documented on problem list.
Discharge instructions
o Patients will receive home medications information at discharge.

After Rush clinical leaders identified and selected high priority outcomes performance measures

for this project, the project director met with the clinical leaders, EHR technical support (TS)

staff and a Rush patient satisfaction manager to determine and discuss monthly metric

measurement methods.

Next, the project director cleaned the medication reconciliation data, calculated and

created individual metric monthly totals and graphs for trending over time. Comparing January,

February and March 2009 baseline data with post implementation data from April 2009 through

April 2010 provided a timeframe to analyze the findings. Determining and displaying data

significance proved to be an important process step followed by creating report templates to

trend results over time followed by key stakeholder meetings to review reports and identify gap

existence between designed workflows and actual practice.

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Rush University
Conclusion
Rush University’s EHR system implementation provided a significant step in their

transition to provide high quality, patient-centered healthcare. The design and implement an

ongoing process to evaluate clinical metrics project aids in validate the impact of the EHR on the

quality of healthcare. Rush University developed and piloted the following process to evaluate

clinical EHR metrics7:

1. Define objectives.
2. Identify performance measures.
3. Select measurement tools/data sources.
4. Define measurement methods.
5. Collect and analyze data.
6. Determine data significance by use of confidence intervals.
7. Create report templates to present data.
8. Use information to improve performance.
9. Identify gaps between designed workflows and actual practice.

Rush University piloted this process and validated significant changes following
implementation in March 2009 including:

1. An increase in partial medication reconciliation at discharge sustained for six months.
2. An increase in full medication reconciliation at discharge not sustained during the study
period.
3. No increase in patient satisfaction with discharge instructions.
4. An increase in documentation of at least one diagnosis in the problem list sustained for
five months.
5. An increase in updates to problem list documentation not sustained during the study
period.

Metric Sources

Metrics help in ‘Meaningful Use’ alignment and provide Business Intelligence data for

strategic planning decisions. The United States Department of Health and Human Services

(HHS) is the United States government’s principal agency for protecting the health of all

Americans and providing essential human services, especially for those who are least able to

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help themselves. The HHS intends to continue to raise thresholds and expectations to ensure that

Meaningful Use encourages patient-centric, interoperable health information exchange across

provider organizations regardless of provider’s business affiliation or EHR platform. The following

is the HHS’ Electronic Health Records 2011 ‘Meaningful Use’ criteria8.

Improving quality, safety, efficiency and reducing health disparities
Computerized Physician Order Entry (CPOE) used for at least 80 percent of all orders
Implement drug-drug, drug-allergy, drug formulary check function
Up-to-date problem list and active diagnoses (using ICD-9-CM or SNOMED CT®) for at
least 80% of patients (at least one entry or indication of no active problem).
Seventy-five percent of permissible pharmaceutical prescriptions generated and
transmitted electronically with certified EHR technology
Maintain active medication list for at least 80 percent of patients
Maintain active medication allergy list (at least one entry or “none”) for at least 80% of
patients
Record demographics (preferred language, insurance type, gender, race, ethnicity, date of
birth) for at least 80 percent of patients
Record and chart changes in vital signs [height weight, blood pressure, body mass index,
growth chart (children 2 to 20)] for at least 80 percent of patients.
Record smoking status for at least 80 percent of patients (over 13)
Incorporate at least 50 percent of clinical lab test results into EHR
Generate at least one list of patients with a specific condition (for use in quality
improvement, reduction of disparities, and outreach)
Report ambulatory quality measures to CMS (or state Medicaid agency)
Reminders of preventive or follow-up care sent to at least 50 percent of patients age 50
and over.
Implement five clinic decision support rules relevant to practice
Check insurance (public and private) eligibility electronically for at least 80% of patients
Submit at least 80% of claims to public and private insurance plans electronically.
Engaging patients and families in their health care

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Offer patients electronic copies of their health information (with 80% of those who
request copies provided them within 48 hours).
Provide patients timely (within 96 hours) access to their health information (lab results,
problem list, medication list, allergies) to at least 10 percent of patients.

Improving care coordination
Capability to exchange key clinical information (e.g. problem list, medication list,
allergies, diagnostic test results)
Perform medication reconciliation at relevant encounters and at each transition of care
and referral
Provide summary care record of each transition of care and referral
Improving population and public health
Perform at least one test of the certified EHR program’s capacity to submit electronic
data to an immunization registry
Perform at least one test of the EHR system’s capability to provide electronic syndromic
surveillance data to public health agencies.
Conduct a Health Insurance Portability and Accountability Act (HIPAA) security risk
analysis (or review past analysis)

Another source of metrics comes from the National Quality Forum (NQF), a nonprofit
organization that operates under a three-part mission to improve the quality of American
healthcare by:
Building consensus on national priorities and goals for performance improvement
and working in partnership to achieve them;
Endorsing national consensus standards for measuring and publicly reporting on
performance; and
Promoting the attainment of national goals through education and outreach
programs.

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While measures come from many sources, those endorsed by the National Quality Forum

(NQF) have become a common point of reference. An NQF endorsement reflects rigorous

scientific and evidence-based review, input from patients and their families, and the perspectives

of people throughout the healthcare industry. The science of measuring healthcare performance

has made enormous progress over the last decade, and it continues to evolve. The high stakes

demand our collective perseverance. Measures represent a critical component in the national

endeavor to assure all patients of appropriate and high-quality care. Listed below are the EHR

representations of the many NQF measures9.

NQF 0019 Percentage of patients having a medication list in the medical record.

NQF 0020 Percentage of patients having documentation of allergies and adverse reactions in the
medical record.
NQF 0487 Of all patient encounters within the past month that used an electronic health record
(EHR) with electronic data interchange (EDI) where a prescribing event occurred, how many
used EDI for the prescribing event.

NQF 0488 Documents whether provider has adopted and is using health information technology.
To qualify, the provider must have adopted and be using a certified/qualified electronic health
record (EHR).

NQF 0489 Documents the extent to which a provider uses certified/qualified electronic health
record (EHR) system that incorporates an electronic data interchange with one or more
laboratories allowing for direct electronic transmission of laboratory data into the EHR as
discrete searchable data elements.
NQF 0490 Documents the extent to which a provider uses a certified/qualified electronic health
record (EHR) system capable of enhancing care management at the point of care. To qualify, the
facility must have implemented processes within their EHR for disease management that
incorporate the principles of care management at the point of care which include:
a. The ability to identify specific patients by diagnosis or medication use
b. The capacity to present alerts to the clinician for disease management, preventive
services and wellness
c. The ability to provide support for standard care plans, practice guidelines, and protocol

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NQF 0491 Documentation of the extent to which a provider uses a certified/qualified electronic
health record (EHR) system to track pending laboratory tests, diagnostic studies (including
common preventive screenings) or patient referrals. The Electronic Health Record includes
provider reminders when clinical results are not received within a predefined timeframe.
NQF 0648 Percentage of patients, regardless of age, discharged from an inpatient facility to
home or any other site of care for whom a transition record was transmitted to the facility or
primary physician or other health care professional designated for follow-up care within 24 hours
of discharge.

Conclusion

The Healthcare industry is in the midst of converting its current patient healthcare records

system over to an Electronic Health Record (EHR) or Electronic Medical Record (EHR) system.

When properly aligned with the definition of Meaningful Use, through the use of Business

Intelligence and metrics, EHR provides ways of collecting, analyzing and presenting relevant

patient data that will help to improve the patient care processes. These statistics shows a steady

increase in the percentage of office-based physicians with some form of EHR systems in use but

we are still along ways away from becoming having paperless free health records. Efforts must

continue in order to create meaningful metrics to successfully monitor processes and procedures

to not only satisfy Meaningful Use requirements but also patient care satisfaction while

maintaining fiscal responsibility.

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References

1. Meaningful Use Introduction, in Center for Disease Control and Prevention. Retrieved from
http://www.cdc.gov/ehrmeaningfuluse/introduction.html

2. Stefan, Susan. Rush University Medical Center. Evaluation of EHR Clinical Metrics to Demonstrate Quality
Outcomes, in Healthcare Information and Management Systems Society. Retrieved from
http://www.himss.org/content/files/proceedings/2011/NI8.pdf

3. Keeling Terri, L. Issues in Information Systems. Clinical Research: Using Business Intelligence Framework,
Volume XI, No. 1, 2010, 372-376

4. Florida Alcohol & Drug Abuse. FADAA Training. Session 1: Introduction to Electronic Health Records (EHRs)
Retrieved from
http://www.fadaa.org/services/resource_center/PD/WebEx/20110512_EHR/EHR_session_1_training_content.pdf

5. Meyer C. How the Right Measures Help Teams Excel. Harvard Business Review on Measuring Corporate
Performance. 1998:99-122.

6. Stefan, Susan. Focus Quality Outcomes and Patient Safety. Evaluation of Clinical Metrics Medication
Reconciliation, Problem List and Discharge Instructions. Retrieved form
http://www.himss.org/content/files/jhim/24-4/8_STEFAN.pdf

7. Spath PL. (1997). Beyond Clinical Paths: Advanced Tools for Outcomes Management. Chicago: American
Hospital Publishing Inc.

8. HHS proposes EHR ‘meaningful use’ criteria, in Michigan Optometric Association. Retrieved from
http://michigan.aoa.org/documents/mi/EHR_Meaningful_Use.pdf

9. NQF-Endorsed® Standards, in National Quality Forum. Retrieved from
http://www.qualityforum.org/Measures_List.aspx

10. Hsiao Chun-Ju, Ph.D.; Hing ,Esther, M.P.H.; Socey, Thomas C.; and Cai, Bill M.A.Sci., Division of Health Care
Statistics, Electronic Medical Record/Electronic Health Record Systems of Office-based Physicians: United States,
2009 and Preliminary 2010 State Estimates. Retrieved from
www.cdc.gov/nchs/data/hestat/ehr_ehr_09/ehr_ehr_09.pdf

11. U.S. EHR Adoption ModelSM Trends HIMSS Analytics Retrieved from
www.himssanalytics.org/docs/HA_EHRAM_Overview_ENG.pdf

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