The Utility of Clinical Information Systems: Quality Measurement, Patient Safety, and Evidence Based Guidelines Rosemary Kennedy, PhD, RN, MBA, FAAN Vice.

The Utility of Clinical Information Systems: Quality Measurement, Patient Safety, and Evidence Based Guidelines Rosemary Kennedy, PhD, RN, MBA, FAAN Vice President, Health Information Technology National Quality Forum (NQF)

Curriculum Sections National Initiatives Related to Patient Safety and Quality Quality Measurement Overview Informatics Tools to Promote Safety and Measure Quality Using Clinical Information Systems to Measure and Improve Outcomes Using Evidence Based Guidelines

Section One

National Initiatives Related to Patient Safety and Quality

Learning Objectives National Initiatives Related to Patient Safety and Quality At the completion of this session, the learner will: Describe components of the National Quality Landscape Describe national safety and quality organizational initiatives Describe a framework for evaluating safety from a system perspective Identify three key patient safety areas important to improving patient safety Describe features of health information technology that impact safety

Organizations Involved in HealthCare Quality
Institute of Medicine (IOM) The Institute of Medicine (IOM) is an independent, nonprofit organization that works outside of government to provide unbiased and authoritative advice to decision makers and the public involved in healthcare

Six Dimensions of Quality
Six Aims for Improvement Safe - Avoid injuries to patients from care intended to help Effective – Apply scientific knowledge to those who could benefit and refrain from services not likely to benefit Patient-centered – Respect preferences, needs, and values in all decisions Timely - Avoid waits and delays Efficient - Avoid waste in all resource use Equitable – Provide equal treatment independent of personal characteristics IOM Crossing the Quality Chasm, March, 2001

Six Aims for Improvement Safe - Avoid injuries to patients from care intended to help Effective – Apply scientific knowledge to those who could benefit and refrain from services not likely to benefit Patient-centered – Respect preferences, needs, and values in all decisions Timely - Avoid waits and delays Efficient - Avoid waste in all resource use Equitable – Provide equal treatment independent of personal characteristics All Six Aims Must be Addressed in Order to Improve Quality IOM Crossing the Quality Chasm, March, 2001

Outcome Measure Areas for the Six Aims Safe - Analyzing medication error rates Effective – Monitor drugs to avoid using in elderly populations based on evidence based guidelines of care Patient-centered – Measuring patient experiences with care using surveys Timely - Monitoring wait times in outpatient clinics Efficient - Measuring costs of care by resource mix Equitable – Evaluating differences in outcomes by gender and race

Health IT and Patient Safety Building Safety Systems for Better Care
Institute of Medicine Health IT and Patient Safety Building Safety Systems for Better Care Charge to the committee Summarize existing knowledge of the effects of health IT on patient safety Make recommendations to HHS regarding specific actions federal agencies should take to maximize the safety of health IT–assisted care Make recommendations concerning how private actors can promote the safety of health IT–assisted care, and how the federal government can assist private actors in this regard IOM, November 10, 2011

IOM Building Safer Systems
Building Safer Systems - Key IOM Findings Health information technology (IT) can improve patient safety in some areas such as medication safety Significant gaps in the literature regarding how health IT impacts patient safety overall Safer implementation and use begins with viewing health IT as part of a larger sociotechnical system All stakeholders need to work together to improve patient safety Literature has shown that health IT may lead to safer care and/or introduce new safety risks Magnitude of harm and impact of health IT on patient safety is not well known because: –Heterogeneous nature of health IT products –Diverse impact on different clinical environments and workflow –Legal barriers and vendor contracts –Inadequate and limited evidence in the literature IOM Crossing the Quality Chasm, March, 2001

Safety as a System Property
Safety is a characteristic of a sociotechnical system System-level failures occur almost always because of unforeseen combinations of component failures

Features of Safety Health Information Technology
Health Professionals, Health Care Organizations, Vendors All stakeholders must coordinate efforts to identify and understand patient safety risks associated with health IT by: Facilitating the free flow of information Creating a reporting and investigating system for health IT–related deaths, serious injuries, or unsafe conditions Researching and developing standards and criteria for safe design, implementation, and use of health IT Safety Systems For Health IT

Agency for Healthcare Research and Quality (AHRQ) AHRQ’s mission is to improve the quality, safety, efficiency, and effectiveness of health care for all Americans As 1 of 12 agencies within the Department of Health and Human Services, AHRQ supports research that helps people make more informed decisions and improves the quality of health care services Working with the public and private sectors, AHRQ builds the knowledge base for what works—and does not work—in health and health care and translates this knowledge into everyday practice and policymaking

Agency for Healthcare Research and Quality (AHRQ) Information on comparative effectiveness of drugs, medical devices, tests, surgeries, or ways to deliver health care Quality improvement and patient safety research and dissemination Support to promote access to and encourage the adoption of health IT Prevention and care management - translates evidence-based knowledge into recommendations to improve the health of all Americans Health care value - produces the measures, data, tools, evidence, and strategies that stakeholders need to improve health care AHRQ's focus areas are: Comparing the effectiveness of treatments. AHRQ's patient-centered outcomes research improves health care quality by providing patients and physicians with state-of-the-science information on which medical treatments work best for a given condition. Comparisons of drugs, medical devices, tests, surgeries, or ways to deliver health care can help patients and their families understand what treatments work best and how their risks compare. AHRQ initiatives include: The John M. Eisenberg Center for Clinical Decisions and Communications Science that translates comparative effectiveness reviews and research reports created by AHRQ's Effective Health Care Program into guides and tools for consumers, clinicians, and policymakers. Evidence-based Practice Centers that review and synthesize scientific evidence for conditions or technologies that are costly, common, or important to the Medicare or Medicaid programs. The Centers for Education and Research on Therapeutics that conduct research and provide education to advance the optimal use of drugs, biologicals, and medical devices. The Developing Evidence to Inform Decisions about Effectiveness Network, research-based health organizations that conduct practical studies about the outcomes, comparative clinical effectiveness, safety, and appropriateness of health care items and services. Quality improvement and patient safety. AHRQ funds and disseminates research that identifies the root causes of threats to patient safety, provides information on the scope and impact of medical errors, and examines effective ways to make system-level changes to help prevent errors. AHRQ initiatives include: Preventing healthcare-associated infections. Medical liability reform. Patient Safety Organizations, which collect and analyze patient safety events that health care providers report and provide feedback to help clinicians and health care organizations improve health care quality. TeamSTEPPS® (Team Strategies and Tools to Enhance Performance and Patient Safety), an evidence-based teamwork system designed to improve communication and teamwork skills among health care professionals. Patient safety culture assessment tools that hospitals, nursing homes, and medical offices use to assess their patient safety culture, track changes in patient safety over time, and evaluate the impact of specific patient safety interventions. Health information technology. AHRQ's health information technology (health IT) initiative is part of the Nation's strategy to put technology to work in health care. AHRQ provides support to promote access to and encourage the adoption of health IT. The broad mission of AHRQ's health IT initiative is to improve the quality of health care for all Americans. The Agency has focused its health IT activities on the following three goals: Improve health care decision-making. Support patient-centered care. Improve the quality and safety of medication management. Prevention and care management. AHRQ translates evidence-based knowledge into recommendations for clinical preventive services to improve the health of all Americans. AHRQ initiatives include: The U.S. Preventive Services Task Force, an independent panel of nationally renowned, non-Federal experts in prevention and evidence-based medicine that assesses the benefits and harms of preventive services and makes recommendations about which preventive services should be incorporated routinely into primary care practice. The Patient-Centered Medical Home, a model to improve health care by transforming how primary care is organized and delivered that ensures care is patient-centered, comprehensive, accessible, coordinated across the health care system, and uses a systems-based approach to quality and safety. The Practice-Based Research Network that rapidly develops and assesses methods and tools to ensure that new scientific evidence is incorporated into real-world practice settings. Health care value. AHRQ finds ways to achieve greater value in health care by producing the measures, data, tools, evidence, and strategies that health care organizations, systems, insurers, purchasers, and policymakers need to improve the value and affordability of health care. AHRQ initiatives include: The Medical Expenditure Panel Survey, a family of surveys that gather information about families and individuals, their medical providers, and employers across the United States and is the only national source of annual data on the specific health services that Americans use, how frequently services are used, the cost of services, and the methods of paying for services. The Healthcare Cost and Utilization Project, a family of health care databases and related software tools and products developed through a Federal-State-industry partnership that serves as a national information resource of patient-level health care data. Quality Indicators that highlight potential quality concerns; identify areas that need further study; and track changes over time in prevention, inpatient care, patient safety, and pediatric care. The annual National Healthcare Quality Report and National Healthcare Disparities Report that measure trends in effectiveness of care, patient safety, timeliness of care, patient centeredness, and efficiency of care. State Snapshots that provide, via a Web site, State-specific health care quality information, including strengths, weaknesses, and opportunities for improvement. The Consumer Assessment of Healthcare Providers and Systems program that develops surveys to help improve the quality of health care from the perspective of consumers and patients. The National Guideline Clearinghouse™, a Web-based resource for information on more than 2,300 evidence-based clinical practice guidelines. The National Quality Measures Clearinghouse™, a database and Web site for information on specific evidence-based health care quality measures and measure sets

National Quality Forum The National Quality Forum (NQF) is 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.

National Priorities Partnership
The NPP is a collaborative effort of 51 major national organizations—which brings together public- and private-sector stakeholder groups in a forum that balances the interests of consumers, purchasers, health plans, clinicians, providers, communities, states, and suppliers in achieving the aims of better care, affordable care, and healthy people and communities

Aligning Accountability Programs with Value:
The Performance Measurement Enterprise Priorities and Strategies Standardized Measures Electronic Data Platform Alignment of Environmental Drivers Evaluation and Feedback National Quality Strategy National Priorities Partnership High Impact Conditions NQF Endorsement Process Quality Data Model Measure Authoring Tool Measures Applications Partnership Quality Positioning System Measure Use Impact on Health, Health Care, and Cost

Measurement Requirements for HIT
HITAC framework NQF’s Health Information Technology Advisory Committee (HITAC) developed a framework to describe the breadth of information needed to measure health. The framework was envisioned to help drive the data platform to provide the information required to improve health from the perspective of measurement. The framework provides the basis for a common information model to describe data reusable for different It sets requirements based on the need to help drive future development of mechanisms to capture and access such information. For the near term, the companion, QDM Style Guide June 2012 provides guidance to measure developers to create feasible measures for data that can be expected in EHRs The Framework incorporates four domains of information to enable a broader reach for data and encourage attention to the entire spectrum from the perspective of data infrastructure: Individual Characteristics (encompassing the Behaviors, Social / Cultural Factors, Preferences, and Personal Resources), Health Related Experience (with the perspectives of patient, consumer, and care giver), Clinical Care Process (including proteomic and genomic data), and Community / Environmental Characteristics. Each of these dimensions has an individual consumer, a population (previously, community), and health system dimension – factors that can be attributed to the individual and factors that are influenced by local community and population demographics. It is likely that any comprehensive measure of health should address each of the dimensions. The information requirements for each dimension are grounded in sources such as EHRs, PHRs, HIEs, Public Health surveys, and other sources. The HITAC QDM Health Information Framework is the conceptual platform on which the QDM structure is built. It encompasses data from EHRs and other sources to manage measures of health for populations, health plan members, health system participants (or an individual provider’s panel of patients), employers, or for measures of individual health for consumers. Examples of the many data sources are listed in Figure 2 (EHRs, PHRs, Health Information Exchanges (HIEs), public health surveys, and registries), but these are not intended to be exclusive. Information obtained from social media, hand-held and other devices will be increasingly significant for measuring health. The QDM is a model, or a grammar, to describe the information requirements (the model of meaning), based on the Framework, that can encourage innovation in data capture (multiple models of use) to enable easier access to data and analysis of health. It is based on a patient-centered approach to health with careful attention to outcomes and patient engagement. The Framework is intended to encourage a more data-driven approach to health information applications to allow greater data sharing and transparency of health outcomes through measurement.

The Joint Commission Accredits and certifies more than 19,000 health care organizations and programs in the United States In 2002, The Joint Commission established its National Patient Safety Goals (NPSGs) program to help accredited organizations address specific areas of concern in regards to patient safety

Sources of Nursing Sensitive Quality Measures
ANA NDNQI (American Nurses Assoc National Database for Nursing Quality Indicators – 1500 Hospitals Contribute) CMS (Center for Medicare and Medicaid Services) NQF (National Quality Forum – Private public partnership for identification and endorsement of quality measures) TJC (The Joint Commission) CDC (Center for Disease Control) NQA (Nursing Quality Alliance) Patient centric and nursing sensitive

National Database of Nursing Quality Indicators® (NDNQI®) NDNQI’s mission is to aid the registered nurse in patient safety and quality improvement efforts by providing research-based national comparative data on nursing care and the relationship to patient outcomes NDNQI is a proprietary database of the American Nurses Association. The database collects and evaluates unit- specific nurse-sensitive data from over 1500 hospitals in the United States NDNQI’s nursing-sensitive indicators reflect the structure, process, and outcomes of nursing care Retrieved from

What are the benefits of NDNQI?
National comparison data Widely collected data Reports within six weeks Research opportunities Secure, user-friendly web-based data entry Ability to trend data RN survey tool Training Continue education credits Publishing

What are the NDNQI indicators?
Patient falls Patient falls with injury Pressure ulcers: Community acquired Hospital acquired Unit acquired Skill mix Nursing hours per patient day RN Surveys: Job Satisfaction Practice Environment Scale RN education & certification Pediatric pain assessment cycle Pediatric IV infiltration rate Psychiatric patient assault rate Restraints prevalence Nurse turnover Healthcare-associated infections: Ventilator-associated pneumonia (VAP) Central line-associated blood stream infection (CLABSI) Catheter-associated urinary tract infections (CAUTI)

What are some uses for NDNQI Reports?
Quality improvement. Unit-level comparisons of staffing data and patient outcomes with units in like hospitals Satisfy reporting requirements, e.g. for Joint Commission or the Magnet Hospital program RN retention efforts RN recruitment Patient recruitment Nursing administration (budget planning, resource allocation) Staff education Research

Data Standards and Organizations
NCVHS - National Committee on Vital and Health Statistics External Advisory Committee to Secretary and Data Council, DHHS; consists of 16 members with overlapping four-year terms. NCHS serves as Executive Secretary Committee was established in 1949 in response to recommendation by the World Health Organization. Rechartered in January 1996 to include a more direct focus on data standardization and privacy activities

WHO Collaborating Center for the Classification of Diseases for North America Established in 1974 at the National Center for Health Statistics (NCHS) WHO is a specialized agency of the United Nations Its responsibilities include the International Classification of Diseases This is performed in conjunction with collaborating centers, established primarily on the basis of language, in North America (NCHS), England, Australia, Paris, Nordic countries (Uppsala), Moscow, Beijing, Caracas, Sao Paulo and Kuwait. Other countries, such as Japan, have recognized ICD offices

NDNQI and CMS Reporting

World Health Organization (WHO)

Key Safety Areas of National Focus
Improve the accuracy of patient identification Improve the effectiveness of communication among caregivers Improve the safety of using medications Reduce the risk of health care-associated infections Reduce the risk of patient harm resulting from falls Prevent health care-associated pressure ulcers (decubitus ulcers) Reduce health care-associated infection Reduce injuries due to surgical and anesthesia errors Reduce injuries due to medical devices Reduce unsafe injection practices and blood products, Reduce unsafe practices for pregnant women and newborns World Health Organization Patient Safety Curriculum

CMS Never Events CMS Never Events
First introduced in 2001 by Ken Kizer, MD, former CEO of the National Quality Forum (NQF), in reference to particularly shocking medical errors (such as wrong-site surgery) that should never occur List has expanded to signify adverse events that are unambiguous (clearly identifiable and measurable), serious (resulting in death or significant disability), and usually preventable NQF initially defined 27 such events in 2002 and revised and expanded the list in 2006 The list is grouped into six categorical events: surgical, product or device, patient protection, care management, environmental, and criminal

Section One Questions Describe one initiative of the IOM
Identify specific outcome measures for each of the six IOM aims for improvement Identify one component of failure within the system property of safety Identify one of the priorities identified by the national priorities partnership and the role health information technology can play Identify one AHRQ initiative areas Identify one of the Never Event safety areas Identify one feature of health IT that impacts safer care What are the benefits of NDNQI? What are the NDNQI indicators? What are some uses for NDNQI Reports?

Teaching Methods and Strategies
Lecture Discussion Board Present safety and quality scenarios within healthcare organizations Present challenges student may face as a member of an organizational safety/quality committee White papers answering questions posed on the prior slide Development of use cases for each of the safety areas showing how one of the organizations provides tools for improving safety Review of public testimonies and calls for comments – have the students respond to the national posts for comments

Questions & Discussion

Section Two

Quality Measurement Overview

Learning Objectives Informatics Tools to Measure Quality and Promote Safety At the completion of this session, the learner will: Describe health care quality Identify challenges and benefits related to using aggregated coded data for quality measurement Describe the types of performance management initiatives and the role for technology Describe the relationship between quality measurement and clinical decision support

Health Care Quality – What Is It?
Donabedian: “The balance of health benefits and harm is the essential core of a definition of quality” (1990). IOM Committee to Design a Strategy for Quality Review and Assurance in Medicare: “Quality of care is the degree to which services for individual and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge How care is provided should reflect appropriate use of the most current knowledge about scientific, clinical , technical, interpersonal, manual, cognitive and organization and management elements of health care.” (Lohr, 1990)

Donabedian’s Framework - 1966
Structure - people, qualifications, organizational structure, targeted to provide high quality care Process - quality of process can vary on two dimensions: appropriateness and skill Outcomes - capture whether healthcare goals were achieved: quality, safety, cost-effectiveness Structure, Process and Outcomes Structure ~ assume that well-qualified people are working in well-appointed and well-organized settings will provide high quality care Process ~ quality of process can vary on two dimensions: appropriateness and skill Outcomes ~ capture whether healthcare goals were achieved: for technical aspects, usually refer to health status indicators (issues of multiple intervening variables); also need to look at cost-effectiveness.

Quality Measurement Challenges and Opportunities

Challenges of Measurement Using Electronic Data
Current Challenges of Measurement Using Electronic Data Underutilization of health IT system capabilities, such as use of structured data fields Clinical workflow barriers, which lead to limited attention to and documentation of coordination processes Lack of data standardization, in particular coding of lab results and medication information Limited health IT system interoperability Agency for Healthcare Research and Quality, Prospects for Care Coordination Measurement Using Electronic Data Sources, AHRQ Publication No EF, March 2012

Challenges of Measurement Using Electronic Data
Current Challenges of Measurement Using Electronic Data (continued) Unknown clinical data quality in various electronic data sources Limitations in linking data Technical hurdles to accessing data Business models related to Health IT that facilitate competition rather than cooperation, especially in ways that prevent a full picture of the steps taken to care for a patient across settings and time Agency for Healthcare Research and Quality, Prospects for Care Coordination Measurement Using Electronic Data Sources, AHRQ Publication No EF, March 2012

Case Example Percentage of heart failure patients discharged home with written instructions or educational material given to patient or caregiver at discharge 43 43

Retrieving Information for Quality Measurement
It is conservatively estimated that centers spend minutes per heart failure case to abstract the data, which in aggregate amounts to more than 400,000 person-hours spent each year by US hospitals Fonarow, G., & Peterson, E. (2009). Heart Failure Performance Measures and Outcomes: Real or Illusory Gains. JAMA, 7, 44 44

Retrieving Information for Quality Measurement
Mostly retrospective Data are in different sources Different kinds of data that do not map Humans are “creating” the data Fonarow, G., & Peterson, E. (2009). Heart Failure Performance Measures and Outcomes: Real or Illusory Gains. JAMA, 7, 45 45

Burden Shifts from Abstractor to Point of Care
Quality Measurement To Using Electronic Point of Care Documentation For Quality Measurement Shift From Using Claims Data And Chart Audits for Quality Measurement Burden Shifts from Abstractor to Point of Care

Advantages of Measurement Using Electronic Data
Electronic Data Offer Minimal data collection burden Structured data may be automatically extracted for quality measurement Rich clinical context For example, the presence of an up-to-date problem list in an EHR should remove the need to look for other means to determine if a patient has a particular diagnosis (e.g., medications used to treat the condition) and allow for more population-based views of care. The presence of a longitudinal record that can trend numerical clinical results should allow measures to better evaluate care based on outcomes without adding additional variables. Agency for Healthcare Research and Quality, Prospects for Care Coordination Measurement Using Electronic Data Sources, AHRQ Publication No EF, March 2012

Advantages of Measurement Using Electronic Data
Electronic Data Offer (continued) Health IT systems populated with clinical data (e.g., evidence-based orders, plans of care, patient response to treatment) offer a view of processes of care and clinical outcomes not possible from data sets based only on claims data Longitudinal patient data aggregated from multiple sources over time. EHRs, PHRs, and HIEs aim to aggregate information from multiple providers, settings, and payers into a single location Agency for Healthcare Research and Quality, Prospects for Care Coordination Measurement Using Electronic Data Sources, AHRQ Publication No EF, March 2012

Types of Performance Management Initiatives
Simple Process Complex Processes Improve Quality Improve HEDIS rates Mammography Pap Smears Immunizations Improve survival rates for cancer or AIDS Improve control of blood sugar for mile to moderate risk diabetics Primary prevention of CAD events by reducing cardiovascular risk profile Improve Quality and Reduce Costs Beta blockers for patients who had a heart attack Improve management of patients at high risk for hospital admission High risk asthmatics Class III of IV heart failure First six months after heart attack Discharge planning for hospitalized patients Reduce Costs Increase use of generic and in-formulary drugs Avoid unneeded referrals and radiology studies CT, MRI during firs t month of acute low back pain Attempts to attract only healthy members Attempts to provide physicians with incentives to order and refer less Typical Methods Evidence based guidelines Reminders, alerts, Performance measurement with process variables Consensus based algorithms and protocols Patient education Care managers Enabling Technologies Reminders integrated into electronic health records Data warehouse with comparative quality measures Protocol driven team based are supported by workflow automation Outcomes data collection systems including patient satisfaction and patient reported outcomes

What Makes a Quality Measure Worth Measuring?

What Makes a Quality Measure Worth Measuring?
Measurement is based on: An established need to change the status quo (e.g., insufficient care, too much care, unsafe care or less than desirable outcomes) for which evidence shows that a change is effective Research which drives evidence-based guidelines of care Research A research study is a process that records information (data) for a group of people to answer questions about a health care problem Definitions of types of studies used to evaluate evidence for measurement are available from the Agency for Healthcare Research and Quality (AHRQ) and the National Cancer Institute (NCI) The measure would need to ask for all data required (the birth date, the sex, the most recent height and the most recent systolic blood pressure) and provide the NHLBI charts with a string of code that any EHR can read to perform the calculation for reporting. Alternatively, EHR products could provide the feature as a standard component, but since that is not a consistent EHR process we can’t rely on it for our measure. That is why many measures rely on information that can be expected in existing EHR products. Based on the information we just reviewed, we now tell our measure developer that we have two options. First is to encourage better standard use of EHRs and work with some vendors to include pediatric blood pressure percentile ranking because it adds value to clinical care. Our second option is to abandon the measure or look for other information that might support our needs. For this hypothetical case we will take the first option and work with some vendors to develop best practices (evidence) and encourage other vendors and providers to follow their example.

Evidence-Based Practice Guidelines
Evidence Based Guidelines Patients similar to those in the clinical studies are generally included. Those who are not, are excluded Criteria: gender, age, type of disease being treated, previous treatments, used as inclusion or exclusion criteria Grading method developed and maintained by the US Preventive Services Task Force (USPSTF) Assigns letter grades to its recommendations (A, B, C, D, and I). ‘A’ has the strongest support and ‘D’ is not supported USPSTF further ranks the certainty (the level of evidence) as high, moderate, or low Medical specialty societies, government agencies, and other organizations develop clinical practice guidelines intended to help providers and patients directly apply the findings of the research into the care patients receive.16 Because clinical studies often carefully select patients for evaluation the guidelines developed from them generally are careful to recommend treatments only to patients that are similar to those evaluated in the studies. Those patients who are similar to those in the clinical studies are generally included. That means a guideline based on a study of treatment for a specific disease in patients under age 65 might apply only to patients 65 and under. Patients over the age of 65 may be excluded. Criteria such as gender, age, type of disease being treated, previous treatments, and other medical conditions can be used as inclusion or exclusion criteria15It has become increasingly common for clinical practice guidelines to carefully evaluate the strength of the evidence for each recommendation (based on the number and types of research studies) and to provide grade the recommendations. A carefully developed evaluation and grading method was developed and is maintained by the US Preventive Services Task Force (USPSTF). The USPSTF assigns letter grades to its recommendations (A, B, C, D, and I). An ‘A’ recommendation has the strongest support and ‘D’ is not supported; ‘I’ is inconclusive).16 The USPSTF further ranks the certainty (the level of evidence) as high, moderate, or low.

Evidence-Based Practice Guidelines
Agency for Healthcare Research and Quality (AHRQ) National Guideline Clearinghouse Translating research into practice can take up to two decades Guidelines can be integrated into EHRs to influence provider behavior at the point of care Actions designed to provide that influence are often called clinical decision support (CDS) Clinical practice guidelines have been available for some time. A good source for established clinical practice guidelines is the Agency for Healthcare Research and Quality (AHRQ) National Guideline Clearinghouse.17 But changing practice based on the research and the guidelines does not happen automatically. Translating research into practice can take up to two decades.18 Even with good evidence and clinical practice guidelines, a large percentage of people in the United States were still not receiving routine preventive services in For that reason, many have put their hopes in the electronic health record (EHR) to help turn the tide and deliver the right care at the right time. EHRs provide the opportunity to influence the provider’s behavior at the time he or she interacts with it to enter or retrieve information. Actions designed to provide that influence are often called clinical decision support (CDS). Much has been written about CDS and the reader is referred to other chapters of this book and other sources for further details20

What is the Connection between Clinical Decision Support and Quality Measurement?

What is the Connection between Quality Measurement and Clinical Decision Support?
Evaluates whether or not the expected services were provided or whether the patient’s status improved as expected Answers questions: “What percentage of the provider’s patients with diabetes had the test done and how many had results in normal range?” Clinical Decision Support Efforts to influence behavior at the right time within the process of care CDS relies on triggers that initiate a rule, input data that the rule uses to evaluate what needs to happen, interventions that the rule tells the computer system to do to give the provider the action steps he or she can take to help the patient improve It is important to note the direct connection between CDS, efforts to influence behavior at the right time within the process of care, and quality measurement that evaluates whether or not the expected services were provided or whether the patient’s status improved as expected. For example, CDS helps to make sure a diabetic patient has a hemoglobin A1c (HbA1c) blood test to make sure his or her diabetes is controlled over time. After all is said and done, quality measurement evaluates if the test was performed and if the result shows good control of the patient’s diabetes. Both rely on the same information, that the HbA1c blood test was ordered, processed by the clinical laboratory, and that the result is available and in normal range. But each uses that information differently. If CDS determines the test was not performed or the result is out of range, it can be programmed to encourage the provider to order the test, or if the result is high to take action to improve the patient’s blood sugar control. The quality measure uses the same information to see, over time, what percentage of the provider’s patients with diabetes had the test done and how many had results in normal range. CDS relies on triggers that initiate a rule, input data that the rule uses to evaluate what needs to happen, interventions that the rule tells the computer system to do to give the provider the action steps he or she can take to help the patient improve (Figure 1).

Clinical Decision Support – Four Components

Quality Measurement and Clinical Decision Support
Close linkage between quality measures and clinical decision support Both are driven by the same clinical knowledge Each requires similar data and each plays a role in evaluating clinical performance

What is the Measure of a Measure?
To ensure that measures meet the needs of those who use them, it is important to manage the quality, the impact and the value of the information they produce. Regulatory and governmental organizations require the reporting of financial and clinical patient-level data for performance measurement. Some of these programs incorporate a pay-for-performance component such that healthcare organizations and clinicians are held accountable for their performance by variation in reimbursement.22 The Affordable Care Act allows the Center for Medicare and Medicaid Service (CMS) to develop new models of payment for care that shares cost savings between CMS and the Accountable Care Organizations (ACOs). These payments are directly linked to the ACO’s performance in quality measures that affect patient and caregiver experience of care, care coordination, patient safety, preventive health and health provided for at-risk populations and the frail elderly.23 To be sure the measures used have the impact expected to support the ACO programs, a stringent process of review is needed. National Quality Forum (NQF) supports that need by using a formal Consensus Development Process (CDP) to endorse measures for quality performance and public reporting.24

Criteria for Quality Measures
Important to measure and report: the extent to which the measure focus is important to make significant gains in healthcare quality (safety, timeliness, effectiveness, efficiency, equity, patient centeredness) Scientific acceptability: the extent to which the results of the measure are consistent (reliable) and credible (valid) if it is implemented as specified. Usability: Extent to which those who will use the measure can understand the results and use them to make meaningful decisions Feasibility: Extent to which the data required to compute the measure are readily available without undue burden, and can be implemented Criteria are summarized below, including importance to measure, scientific acceptability, usability and feasibility: Importance to measure and report: the extent to which the measure focus is important to make significant gains in healthcare quality (safety, timeliness, effectiveness, efficiency, equity, patient centeredness) and to improve health outcomes for a specific high-impact aspect of healthcare where there is variation in overall poor performance. Scientific acceptability of the measure properties: the extent to which the results of the measure are consistent (reliable) and credible (valid) if it is implemented as specified. Scientific acceptability assessment includes review of reliability, validity, exclusion criteria, risk assessment strategy, scoring methods, the comparability of multiple data sources, and the methods to determine potential disparities in care provided. Also highly important are the extent to which those who will use the measure can understand the results and use them to make meaningful decisions (usability) and the extent to which the data required to compute the measure are readily available without undue burden, and can be implemented (feasibility).

Criteria for Measures Measure Application Partnership
NQF convenes the Measure Application Partnership (MAP), a public-private partnership to providing input to the US Department of Health and Human Services (HHS) about what performance measures it should select for public reporting and performance-based payment programs as required in the Affordable Care Act. Guided by the National Quality Strategy, measures are recommended that address national healthcare priorities and goals, such as making care safer and ensuring each person and family are engaged as partners in their care. Criteria are summarized below, including importance to measure, scientific acceptability, usability and feasibility: Importance to measure and report: the extent to which the measure focus is important to make significant gains in healthcare quality (safety, timeliness, effectiveness, efficiency, equity, patient centeredness) and to improve health outcomes for a specific high-impact aspect of healthcare where there is variation in overall poor performance. Scientific acceptability of the measure properties: the extent to which the results of the measure are consistent (reliable) and credible (valid) if it is implemented as specified. Scientific acceptability assessment includes review of reliability, validity, exclusion criteria, risk assessment strategy, scoring methods, the comparability of multiple data sources, and the methods to determine potential disparities in care provided. Also highly important are the extent to which those who will use the measure can understand the results and use them to make meaningful decisions (usability) and the extent to which the data required to compute the measure are readily available without undue burden, and can be implemented (feasibility).

Section Two Questions What’s the definition of health care quality?
What are the three components of Donabedian’s framework? Name two challenges related to obtaining data necessary for quality measurement Name two advantages of using electronic clinical information systems for quality measurement What’s the relationship between quality measurement and clinical decision support? What’s the relationship between quality measurement and evidence-based guidelines of care? What are the four components of a clinical decision support framework? What are the criteria for evaluating quality measures?

Section Three

Informatics Tools to Measure Quality and Promote Safety

Learning Objectives Informatics Tools to Measure Quality and Promote Safety At the completion of this session, the learner will: Define data, information, and knowledge Describe components of an architecture framework for clinical information systems Describe the value of structured data models Describe standards necessary for the electronic health record

Foundational Framework for the Benefits of Clinical Information Systems (CIS)
DIWK framework. Reprinted with permission from Nelson.

Architecture Framework for Clinical Information Systems
Analysis Reporting Clinician Workstation Administrative Systems Clinical Systems Analytical Data Repository Clinical Data Repository Workflow Clinical Decision Support (Evidence Based Guidelines) Terminology Infrastructure ( Data and Information)

Definitions Rules Engine
Software that automates policies and procedures within an organization, whether legal, internal or operational Requires placing the rules in an external repository that can be easily reviewed rather than buried inside the code of numerous applications Instead of a program executing internal algorithms, it goes out to the rules engine to obtain its business logic

Definitions Common Vocabulary Engine
Allows for the definition of terms and relationships, which can then be used for the definition of clinical protocols, clinical applications, quality reporting and research Allows for transformation and abstraction of data Contains all the clinical concepts needed for healthcare delivery, measurement, and research Pedersen, T. & Jensen, C. Clinical data warehousing—A survey. Retrieved from http%3A%2F%2Flibrary.med.utah.edu%2Fcyprus%2Fproceedings%2Fmedicon98%2Fmedicon98.pedersen.t orben.pdf&ei=VToTUP_2MtLxqQHNiIGoDg&usg=AFQjCNHJt_oJLTJgXsUdzeOrZQLez6FdXQ

Architecture Framework for Clinical Information Systems
Performance Reporting Clinician Workstation Administrative Systems Clinical Systems Analytical Data Repository Clinical Data Repository Workflow Engine Clinical Decision Support (Evidence Based Guidelines) Rules Engine Terminology Infrastructure ( Data and Information) Terminology Engine

Value Through Structured Terminology
A Foundation to Achieve Knowledge integration of evidence-based guidelines Documentation flexibility Care Coordination Quality measurement Nursing visibility Knowledge discovery Agency for Healthcare Research and Quality, Prospects for Care Coordination Measurement Using Electronic Data Sources, AHRQ Publication No EF, March 2012

What Standards Go Into the Common Vocabulary Engine
What Standards Go Into the Common Vocabulary Engine? What Standards are Needed for the Electronic Health Record?

LOINC - Logical Observation Identifier Names and Codes LOINC was developed at Regenstrief Institute for laboratory and clinical observation coding It is a universal code system for identifying laboratory and clinical observations It is available free-of-charge

LOINC - Logical Observation Identifier Names and Codes Example Fall and injury risk assessment instrument The concepts contained in the instrument are well represented by Clinical LOINC and the UMLS Associated concepts have been identified in the existing clinical information system data dictionary for pre- population of the instrument

SNOMED International - Systematized Nomenclature of Human and Veterinary Medicine A structured nomenclature and classification of the terminology used in human and veterinary medicine developed by the College of Pathologists and American Veterinary Medical Association Terms are applied to one of eleven independent systematized modules SNOMED CT is owned, maintained and distributed by the International Health Terminology Standard Development Organisation (IHTSDO)

UMLS - Unified Medical Language System Under development by the National Library of Medicine as a standard health vocabulary Includes a Metathesaurus, a Semantic Network and an Information Sources Map The purpose of the UMLS is to help health professionals and researchers retrieve and integrate electronic biomedical information from a variety of sources, irrespective of the variations in the way similar concepts are expressed in different sources and classification systems Has incorporated most source vocabularies. Large-scale testing is taking place

Section Three Questions
Describe the definition of data, information, and knowledge Identify one benefit of using aggregated code data for quality measurement Describe one of the standards necessary to extract data for performance measurement and improvement Describe the role of LOINC and SNOMED in coding data for health IT Describe one challenge in extracting data for quality measurement Write the definition of a rules engine and vocabulary engine What is the most important component of the health IT architecture

Section Four

Curriculum Sections Using Clinical Information Systems to Measure and Improve Outcomes Using Evidence Based Guidelines

What Standards Are Needed for Quality Reporting from the Electronic Health Record?

Learning Objectives Standards Needed for Electronic Quality Measurement At the completion of this session, the learner will: Describe standards necessary to extract data for quality measurement and performance improvement Describe an electronic measure (eMeasure) Describe the components of a quality measure Describe the steps related to structuring EHR queries for quality measurement

Quality Measurement in the Clinical Realm
Quality Data Model Measure Authoring Tool eMeasure EHR Real-Time Information to Clinician Electronic Reporting and Sharing Capture Data Provide Care Inform all Stakeholders Current data sources for measures include: Claims data, Clinical data, Patient reported data. With continued EMR and QDM use, we are moving toward the primary data source- EHR and eData (PHR, other). The ideal state moving forward in quality measurement sees measures integrating information from all three sources and measures assessing care provided across settings and providers. The current process for measuring quality using EHRs is evolving. Most existing measures have not been written in a format to allow direct queries to EHRs and extract data. Much of the information needed to evaluate these existing measures is based on information from claims submitted for payment or from human abstractors that review medical records in detail to know if the measure criteria are met. Some measures use clinical data that are available because they are included in claims attachments, specifically laboratory results and pharmacy dispensing data. Using such clinically enriched claims information is a step forward for measurement but it is not able to take advantage of potentially rich information present in the EHR. So to implement measures that describe clinical data, the process continues to use traditional manual methods to interpret the measure to evaluate available information. This effort can include medical record abstraction, natural language processing methods to find information from unstructured (free text) fields in the EHR, or developing specific fields in the EHR for clinicians to enter the data using drop-down menus or check boxes. Adding additional fields for clinician data entry is very costly. It takes significant time and effort, increases the likelihood of incorrect entry of information and it is not part of the patient care workflow so it takes time away from caring for the patient. Using check-boxes further limits the value of the data since the information is an interpretation of what should be captured directly as a result of the patient interaction, i.e., the provider’s statement about that interaction, or confirmation. For example, to check a box that the patient’s systolic blood pressure is less than 140 mmHg makes the provider interpret a result already captured and attest it is correct. Capturing the result itself from the EHR as it is initially entered is preferred since it is more accurate and less prone to error. It also comes with the benefit of knowing more information about that result (metadata) such as the method used to obtain it and the date and time it was performed. There are some data that can only be captured using confirmation, checking the box that a process occurred. A good example is medication reconciliation. Medication reconciliation refers to the process of reviewing the patient's complete medication regimen at the time of admission, transfer, and discharge and comparing it with the regimen being considered for the new setting of care to avoid duplication, inadvertent omission of critical medications and to prevent avoidable interactions among the medications the patient is receiving.28 However, there is no defined set of online clicks that has been determined to automatically know that a provider has reconciled a patient’s medication lists. Therefore, the provider must attest that reconciliation has been performed and is complete by using some sort of check box or signature. Even attempts to reconfigure, or “retool” measures for the EHR platform used concepts intended for a manual abstraction or provider confirmation process. And the underlying standards (discussed later in this chapter) are not yet ready to directly query an EHR database. So there is still manual intervention required to use EHR data for measurement. Figure 3 shows a stylized process for implementing quality measures in EHRs as it exists today. eMeasure: Health Quality Measure Format Develop Performance Measures

Quality Measures What are Quality Measures?
Quality measures are tools that help us measure or quantify healthcare processes, outcomes, patient perceptions, and organizational structure and/or systems that are associated with the ability to provide high-quality health care and/or that relate to one or more quality goals for health care These goals include: effective, safe, efficient, patient- centered, equitable, and timely care

Quality Data Model: Overview
NQF HIT Critical Paths: Care Coordination TEP DRAFT 3/16/2012 Quality Data Model: Overview QDM: What is It? A structure and grammar to represent quality measures precisely and accurately in a standardized format that can be used across electronic patient care systems Role in Quality Measurement Provides a standard way to describe concepts clearly and consistently for use across all quality measures Creates a common language across all healthcare stakeholders so quality measurement data can be consistently represented, captured, and shared across EHRs and other electronic patient care systems Only “standard” for eMeasures that exists today Backbone for the Measure Authoring Tool QDM Background As quality measurement increasingly moves to an electronic platform - Health IT is a core component of the Quality Continuum. Changing how data for quality measurement is collected – moving from using claims data, to use of the EHR – this brings us closer to the primary source that reflects care provided to the patient (and or consumer). In order to accomplish this goal – we need an HIT infrastructure The QDM is a common language that can be used to represent the measure. The QDM is a grammar that defines and describes clinical concepts in a standardized format across all quality measures. The QDM creates a common language among all healthcare stakeholders so quality measurement data can be consistently represented, captured, and shared across EHRs and other electronic information systems. 2010 saw the retooling of 113 paper based measures into electronic specifications (eMeasures); this work enabled some of these eMeasures to be include in stage 1 of Meaningful Use Value of the Quality Data Model in Measure Development value in the Quality Data Model (QDM) as a useful tool to enable measure developers and implementers to identify the data elements within an EMR/ EHR that map to clinical concepts and are available for use within an eMeasure. QDM Style Guide The QDM Style guide was developed in 2012 in response to requests from the measure development community on how to best use the QDM in both retooling existing measures as well as writing de novo measures (with or without the use of the Measure Authoring Tool).

QDM Virtual Forum 10.19.11- Draft Slide Review
QDM in the Clinical Realm Quality Measure Quality Data Model Measure Authoring Tool eMeasure EHR Measure Authoring Tool EHR Real-Time Information to Clinician Standard way to consistently describe concepts for use across all quality measures. Common language so quality measurement data can be consistently represented electronically…….. Current data sources Claims data Clinical data Patient reported data Moving toward Primary data source- EHR and eData (PHR, other) Ideal state Measures integrating information from all three sources Measures assessing care provided across settings and providers Develop Performance Measures Clinical Realm

NQF HIT Critical Paths: Care Coordination TEP DRAFT
3/16/2012 Sample Measure Percentage of patients aged 18 years and older with a diagnosis of CAD who were prescribed a lipid-lowering therapy Initial Patient Population Patients aged 18 years and older before the start of the measurement period. Patients that have a documented diagnosis of coronary artery disease before or simultaneously to encounter date Patients who have at least 2 outpatient or nurse facility encounters during the measurement period Denominator Patients aged 18 years and older with a diagnosis of coronary artery disease Numerator Patients who were prescribed lipid-lowering therapy Exclusions Patients who have documentation of a medical, system or patient reason for not prescribed lipid lowering therapy QDM Background As quality measurement increasingly moves to an electronic platform - Health IT is a core component of the Quality Continuum. Changing how data for quality measurement is collected – moving from using claims data, to use of the EHR – this brings us closer to the primary source that reflects care provided to the patient (and or consumer). In order to accomplish this goal – we need an HIT infrastructure The QDM is a common language that can be used to represent the measure. The QDM is a grammar that defines and describes clinical concepts in a standardized format across all quality measures. The QDM creates a common language among all healthcare stakeholders so quality measurement data can be consistently represented, captured, and shared across EHRs and other electronic information systems. 2010 saw the retooling of 113 paper based measures into electronic specifications (eMeasures); this work enabled some of these eMeasures to be include in stage 1 of Meaningful Use Value of the Quality Data Model in Measure Development value in the Quality Data Model (QDM) as a useful tool to enable measure developers and implementers to identify the data elements within an EMR/ EHR that map to clinical concepts and are available for use within an eMeasure. QDM Style Guide The QDM Style guide was developed in 2012 in response to requests from the measure development community on how to best use the QDM in both retooling existing measures as well as writing de novo measures (with or without the use of the Measure Authoring Tool).

What data elements do we need?
NQF HIT Critical Paths: Care Coordination TEP DRAFT 3/16/2012 What data elements do we need? Patients who… What kind of data are we dealing with? What about the data? How do we define these data? …Are diagnosed with Coronary Artery Disease Diagnosis Active ICD-9 , ICD-10, SNOMED-CT …Were prescribed Lipid-lowering Therapy Medication Administered Order Dispensed RxNorm …Have had at least two encounters during the measurement period Encounters CPT …Are at least 18 years old or older Patient Characteristic LOINC QDM Background As quality measurement increasingly moves to an electronic platform - Health IT is a core component of the Quality Continuum. Changing how data for quality measurement is collected – moving from using claims data, to use of the EHR – this brings us closer to the primary source that reflects care provided to the patient (and or consumer). In order to accomplish this goal – we need an HIT infrastructure The QDM is a common language that can be used to represent the measure. The QDM is a grammar that defines and describes clinical concepts in a standardized format across all quality measures. The QDM creates a common language among all healthcare stakeholders so quality measurement data can be consistently represented, captured, and shared across EHRs and other electronic information systems. 2010 saw the retooling of 113 paper based measures into electronic specifications (eMeasures); this work enabled some of these eMeasures to be include in stage 1 of Meaningful Use Value of the Quality Data Model in Measure Development value in the Quality Data Model (QDM) as a useful tool to enable measure developers and implementers to identify the data elements within an EMR/ EHR that map to clinical concepts and are available for use within an eMeasure. QDM Style Guide The QDM Style guide was developed in 2012 in response to requests from the measure development community on how to best use the QDM in both retooling existing measures as well as writing de novo measures (with or without the use of the Measure Authoring Tool). Quality Data Model

QDM Virtual Forum 10.19.11- Draft Slide Review
QDM in the Clinical Realm Quality Measure Quality Data Model Measure Authoring Tool eMeasure EHR Measure Authoring Tool EHR Real-Time Information to Clinician Standard way to consistently describe concepts for use across all quality measures. Common language so quality measurement data can be consistently represented electronically…….. Current data sources Claims data Clinical data Patient reported data Moving toward Primary data source- EHR and eData (PHR, other) Ideal state Measures integrating information from all three sources Measures assessing care provided across settings and providers Develop Performance Measures Clinical Realm

What’s an eMeasure? An eMeasure is the electronic format for quality measures using the Quality Data Model (QDM) and the Healthcare Quality Measure Format (HQMF), an HL7standard The QDM was formally referred to as the Quality Data Set (QDS). The QDM is an information model that defines and describes clinical concepts in a standardized format to clearly and consistently represent concepts for use across all quality measures. The Measure Authoring Tool (MAT) is a web-based tool that allows measure developers to create standardized electronic measures (eMeasures).

Measure Authoring Tool (MAT)

Measure Authoring Tool (MAT): Overview
MAT: What is It? A web-based, publicly-available tool that allows measure developers to create and maintain quality measures in an electronic format (eMeasures) Role in Quality Measurement Simplify the process of creating an eMeasure Standardize how eMeasures are expressed, for greater comparability Provides a quality measure in a standardized XML file that can be read by both humans and computer systems ng_Tool_(MAT).aspx - Highlight initial launch of MAT: Sept 2011 Extensive training/webinars to contractors and public: MAT overview and Demos, QDM, eMeasure implementation MAT 2012 Update due mid January Overview webinar on key features/functions/changes on Jan 12

Measure Authoring Tool Key Features and Functions
11/17/2011 Measure Authoring Tool Key Features and Functions Create and share eMeasures and their corresponding code lists with other users Create and reuse standard value sets and other measure components, limiting rework as new measures are developed Use the Quality Data Model (QDM) as the grammar and structure to fully define and express eMeasures in a standard way, and Export eMeasures in an EHR-readable format to enable collection of comparable healthcare quality data

Sample Measure Percentage of patients aged 18 years and older with a diagnosis of CAD who were prescribed a lipid-lowering therapy Initial Patient Population Patients aged 18 years and older before the start of the measurement period Patients that have a documented diagnosis of coronary artery disease before or simultaneously to encounter date Patients who have at least 2 outpatient or nurse facility encounters during the measurement period Denominator Patients aged 18 years and older with a diagnosis of coronary artery disease Numerator Patients who were prescribed lipid-lowering therapy Exclusions Patients who have documentation of a medical, system or patient reason for not prescribed lipid lowering therapy

Human Readable - Header

Initial Patient Population =
AND: "Patient Characteristic: birth date" >= 18 year(s) starts before start of "Measurement Period" AND: Count >= 2 of: OR: "Encounter: Nursing Facility Encounter" OR: "Encounter: Outpatient Encounter" AND: OR: "Procedure, Performed: Cardiac Surgery" starts before or during OR: "Diagnosis, Active: CAD includes MI" Denominator = AND: "Initial Patient Population" Denominator Exclusions = None Numerator = OR: "Medication, Active: Lipid Lowering Therapy" OR: "Medication, Order: Lipid Lowering Therapy" during "Measurement Period" Denominator Exceptions = OR: "Medication, Order not done: Medical Reason HL7" for "Lipid Lowering Therapy RxNorm Value Set" OR: "Medication, Order not done: System Reason HL7" for "Lipid Lowering Therapy RxNorm Value Set" Data criteria (QDM Data Elements) "Diagnosis, Active: CAD includes MI" using "CAD includes MI Grouping Value Set ( )" "Encounter: Nursing Facility Encounter" using "Nursing Facility Encounter CPT Value Set ( )" "Encounter: Outpatient Encounter" using "Outpatient Encounter CPT Value Set ( )" "Medication, Active: Lipid Lowering Therapy" using "Lipid Lowering Therapy RxNorm Value Set ( )" "Medication, Order: Lipid Lowering Therapy" using "Lipid Lowering Therapy RxNorm Value Set ( )" "Medication, Order not done: Medical Reason HL7" using "Medical Reason HL7 HL7 Value Set ( )" "Medication, Order not done: System Reason HL7" using "System Reason HL7 HL7 Value Set ( )" "Patient Characteristic: birth date" using "birth date LOINC Value Set ( )" "Procedure, Performed: Cardiac Surgery" using "Cardiac Surgery SNOMED-CT Value Set ( )" Supplemental Data Elements

Computer Readable

Using Health IT to Measure and Improve Outcomes
Considering automated queries for measurement – how to ask questions to an EHR Considering automated queries for measurement – how to ask questions to an EHR Let’s get started by going through two example measures to explain the kind of detail needed to ask a question of an EHR. The EHR is primarily a database. It will provide data only if you ask in a way it can understand. And if the question is somewhat ambiguous so will be the answer, if an answer is possible at all. To start refer to Table 1 for a list of measure components.

Quality Measure Structure
Initial population All patients who share a common set of specified characteristics. Denominator May be identical to the initial population or a subset of it to further specify the purpose of the eMeasure. Denominator Exclusions Information about the patients or events that who should be removed from the eMeasure population and denominator. Exclusions are used to be sure the measure evaluates only those patients for whom the information in the numerator should apply, based on the available evidence. Denominator Exceptions Some measures remove patients or events from the denominator only if the numerator interventions or outcomes are not met. Denominator exceptions allow for adjustment of the calculated score for those providers with higher risk populations. Numerator The interventions (processes) that are expected or the outcome that is expected, based on the evidence, for all members of the denominator.

Using Health IT to Measure and Improve Outcomes
Exercise Considering automated queries for measurement – how to ask questions to an EHR Let’s get started by going through two example measures to explain the kind of detail needed to ask a question of an EHR. The EHR is primarily a database. It will provide data only if you ask in a way it can understand. And if the question is somewhat ambiguous so will be the answer, if an answer is possible at all. To start refer to Table 1 for a list of measure components.

Getting Quality Measure Data from the EHR
Example 1 Identify all children with normal blood pressure First Question: What is the time period of interest? This is needed to identify the population. Let’s assume we are working for the measure developer in this case. We need to break down the question into its component parts to be sure the EHR provides the right information to calculate the result. The first question is, “what is the time period of interest?”

Population All children seen in the office at least twice during the calendar year 2012 However, EHRs do not contain the label children, so we need to specify an age range (ages are in EHRs) Population (updated) All persons whose 18th birthday occurs during the calendar year 2012 and who are seen in the office at least twice during the same year Let’s assume we are working for the measure developer in this case. We need to break down the question into its component parts to be sure the EHR provides the right information to calculate the result. The first question is, “what is the time period of interest?”

Numerator ___________________________________ Denominator Population All persons whose 18th birthday occurs during the calendar year 2012 and who are seen in the office at least twice during the same year Now we are pretty sure we know those of interest to the measure. This population is our denominator, the group that we will evaluate.

Example 1 Identify all children with normal blood pressure But now we find that providers don’t record blood pressure as “normal blood pressure” or “abnormal blood pressure.” Rather, they measure and record every blood pressure as two values, the systolic (the pressure when the heart is beating) and the diastolic (the pressure when the heart is resting between beats). So an example of a blood pressure reading, measured in millimeters of mercury (mmHg), would be recorded as 118 / 74 (the first, higher number is the systolic reading and the second, lower number, is the diastolic reading). So now it is important to define what level of blood pressure is considered normal. We were asked to create the measure because there are a set of charts available based on evidence that the National Heart Lung and Blood Institute (NHLBI) published to define what is normal.1 TIP: Reliable sources of evidence are those produced by Government Agencies and specialty organizations Using those charts a provider can compare any given child’s height, sex and age to find how that child compares to other children in the United States. The result is percentile rank. Children in the 95th percentile or higher for systolic blood pressure are considered to have hypertension (their systolic blood pressure is higher than 95 percent of all children). Those children ranking between the 90th and 95th percentiles are considered to have pre-hypertension, and all those ranking less than the 90th percentile are considered to have normal blood pressure. So now we have a definition and we can state our measure as follows: Providers don’t record blood pressure as “normal blood pressure” or “abnormal blood pressure.” They measure and record every blood pressure as two values, the systolic and diastolic (the pressure when the heart is resting between beats).

Numerator All persons in the population (denominator) whose systolic blood pressure is less than the 90th percentile based on age, sex and height according to the NHLBI blood pressure tables __________________________________________________ Denominator All persons whose 18th birthday occurs during the calendar year and who are seen in the office at least twice during the same year Our measure is now specific, but there are still two missing facts that will cause those who try to use it give different results. First, children seen during a calendar year have several blood pressure readings. Which reading is the one we want the EHR to report – the first, the most recent, an average of all systolic blood pressure readings?

There are Two Missing Facts

Missing Facts Children seen during a calendar year have several blood pressure readings. Which reading is the one we want the EHR to report – the first, the most recent, an average of all systolic blood pressure readings? Most providers don’t record the percentile rank for systolic blood pressure when recording blood pressure values, so the information is not available in the EHR. The EHRs do have fields for systolic blood pressure, height, sex and birth date so all are available to compare to the NHLBI charts and find a percentile rank.

Numerator All persons in the population (denominator) whose most recent systolic blood pressure is less than the 90th percentile based on age, sex and height according to the NHLBI blood pressure tables __________________________________________________ Denominator All persons whose 18th birthday occurs during the calendar year and who are seen in the office at least twice during the same year We decide that the most recent systolic blood pressure reading is best for our measure. When we measure at the end of the year, the most recent will be the last reading during that year, whenever it happened.

Missing Facts Children seen during a calendar year have several blood pressure readings. Which reading is the one we want the EHR to report – the first, the most recent, an average of all systolic blood pressure readings? Most providers don’t record the percentile rank for systolic blood pressure when recording blood pressure values, so the information is not available in the EHR. The EHRs do have fields for systolic blood pressure, height, sex and birth date so all are available to compare to the NHLBI charts and find a percentile rank. The measure would need to ask for all data required (the birth date, the sex, the most recent height and the most recent systolic blood pressure) and provide the NHLBI charts with a string of code that any EHR can read to perform the calculation for reporting. Alternatively, EHR products could provide the feature as a standard component, but since that is not a consistent EHR process we can’t rely on it for our measure. That is why many measures rely on information that can be expected in existing EHR products. Based on the information we just reviewed, we now tell our measure developer that we have two options. First is to encourage better standard use of EHRs and work with some vendors to include pediatric blood pressure percentile ranking because it adds value to clinical care. Our second option is to abandon the measure or look for other information that might support our needs. For this hypothetical case we will take the first option and work with some vendors to develop best practices (evidence) and encourage other vendors and providers to follow their example.

Solutions The measure would need to ask for all data required (the birth date, the sex, the most recent height and the most recent systolic blood pressure) and provide the NHLBI charts with a string of codes that any EHR can read to perform the calculation for reporting EHR products could provide the feature as a standard component, but since that is not a consistent EHR process we can’t rely on it for our measure Encourage better standard use of EHRs and work with some vendors to include pediatric blood pressure percentile ranking because it adds value to clinical care The measure would need to ask for all data required (the birth date, the sex, the most recent height and the most recent systolic blood pressure) and provide the NHLBI charts with a string of code that any EHR can read to perform the calculation for reporting. Alternatively, EHR products could provide the feature as a standard component, but since that is not a consistent EHR process we can’t rely on it for our measure. That is why many measures rely on information that can be expected in existing EHR products. Based on the information we just reviewed, we now tell our measure developer that we have two options. First is to encourage better standard use of EHRs and work with some vendors to include pediatric blood pressure percentile ranking because it adds value to clinical care. Our second option is to abandon the measure or look for other information that might support our needs. For this hypothetical case we will take the first option and work with some vendors to develop best practices (evidence) and encourage other vendors and providers to follow their example.

Redefinition of the Electronic Health Record
The EHR must support care delivery AND quality measurement – all in ‘real time”

Crossing the Quality Chasm
“This sort of change in healthcare will not be evolutionary but revolutionary, like going from water to steam” This vision is a chasm that cannot be crossed in two steps 114 13 October 2009 114

Redefinition of the Electronic Health Record
What was ‘secondary use of data’ is now ‘primary use of data’ Creation of a Strategic Plan for “Data” “If You Can’t be There Yourself, Don’t Send Anyone” Deming 1985 115 13 October 2009 115

Know what to do with the data you have
116 13 October 2009 116

Exponential Growth of Patient Data Available for Quality Measurement

Meaningful Use Stage 2 Proposed Measures

Meaningful Use Stage 2 Quality Measures for Hospitals
Requires eligible hospitals and CAHs to report 24 clinical quality measures from a menu of 49 clinical quality measures, including at least 1 clinical quality measure from each of the 6 domains: Clinical Process/Effectiveness Patient Safety Care Coordination Efficient Use of Healthcare Resources Patient & Family Engagement Population & Public Health

Proposed Meaningful Use Stage 2 Quality Measures
AMI-1 Aspirin at arrival Discharge Instructions AMI-3, ACEI or ARB for LVSD AMI-2 Aspirin Prescribed at Discharge Relievers for inpatient asthma Systemic corticosteroids for inpatient asthma PN-6 Initial Antibiotic Selection for Community-Acquired Pneumonia (CAP) in Immunocompetent Patients PN-3b Blood Cultures Performed in the Emergency Department Prior to Initial Antibiotic Received in Hospital

AMI-5 Beta Blocker Prescribed at Discharge AMI-8a Primary PCI within 90 minutes of Hospital Arrival AMI-7a Fibrinolytic Therapy received within 30 minutes of hospital arrival SCIP-VTE-2 Surgery Patients Who Received Appropriate Venous Thromboembolism (VTE) Prophylaxis Within 24 Hours Prior to Surgery to 24 Hours After Surgery SCIP-Card-2 Surgery Patients on Beta-Blocker Therapy Prior to Arrival Who Received a Beta-Blocker During the Perioperative Period SCIP-Inf-4 Cardiac Surgery Patients with Controlled 6 AM Postoperative Serum Glucose

SCIP-Inf-6 Surgery Patients with Appropriate Hair Remove Home Management Plan of Care Document Given to Patient/Caregiver PICU Pain Assessment on Admission PICU Periodic Pain Assessment Venous Thromboembolism Prophylaxis Intensive Care Unit Venous Thromboembolism Prophylaxis Venous Thromboembolism Patients with Anticoagulation Overlap Therapy

Venous Thromboembolism Patients Receiving Unfractionated Heparin with Dosages/Platelet Count Monitoring by Protocol or Nomogram Venous Thromboembolism Discharge Instructions Incidence of Potentially-Preventable Venous Thromboembolism Venous Thromboembolism Prophylaxis Discharged on Antithrombotic Therapy Anticoagulation Therapy for Atrial Fibrillation/Flutter Thrombolytic Therapy Antithrombotic Therapy by End of Hospital Day Two

Discharged on Statin Medication Stroke Education Assessed for Rehabilitation SCIP-Inf-9 Urinary Catheter Removed on Postoperative Day 1 (POD1) or Postoperative Day 2 (PDO2) with day of surgery being day zero Elective Delivery Exclusive Breast Milk Feeding First Temperature Measured Within One Hour of Admission to the NICU First NICU Temperature < 36 degrees C

Proportion of Infants 22 to 29 Weeks Gestation Treated with Surfactant Who Are Treated Within 2 Hours of Birth Median Time from ED Arrival to ED Departure for Admitted ED Patients ED-3 Median Time from ED Arrival to ED Departure for Discharged ED Patients Admit Decision Time to ED Departure Time for Admitted ED Patients SCIP-Inf-1 Prophylactic Antibiotic Within 1 Hour Prior to Surgical Incision SCIP-Inf-2 Prophylactic Antibiotic Selection for Surgical Patients

SCIP-Inf-3 Prophylactic Antibiotics Discontinued Within 24 Hours After Surgery End Time, 48 Hours for Cardiac Surgery AMI-10 Statin Prescribed at Discharge Healthy Term Newborn Hearing Screening Prior to Hospital Discharge (EHDI-1a) IMM-1 Pneumococcal Immunization (PPV23) IMM-2 Influenza Immunization

Section Four Questions
What are quality measures? What is the Quality Data Model? Why is the human readable output for a measure important? What is the Measure Authoring Tool? What are the five parts of a quality measure? Define one meaningful use quality measure

The Utility of Clinical Information Systems: Quality Measurement, Patient Safety, and Evidence Based Guidelines Rosemary Kennedy, PhD, RN, MBA, FAAN Vice.

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The Utility of Clinical Information Systems: Quality Measurement, Patient Safety, and Evidence Based Guidelines Rosemary Kennedy, PhD, RN, MBA, FAAN Vice.

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