Using Health Care Information Technology to Evaluate and Improve Performance and Patient Outcomes

Chapter 24


Using Health Care Information Technology to Evaluate and Improve Performance and Patient Outcomes




Chapter Contents



Regulatory Reporting Initiatives That Drive Performance Improvement, 646


Relevance of Regulatory Reporting to Advanced Practice Nursing Outcomes


Foundational Competencies in Managing Health Information Technology



Foundational Competencies in Quality Improvement



Strategies for Designing Quality Improvement and Outcome Evaluation Plans for Advanced Practice Nursing



Conclusion


The health care reform debate of this decade has generated countless hours of discussion on what the U.S. health care system will look like and how it will be paid for. Even with major steps forward, such as the Patient Protection and Affordable Care Act (PPACA; U.S. Department of Health and Human Services [HHS], 2011) and the creation of the Center for Medicare & Medicaid Services (CMS) Innovation, which is designed to stimulate new ways to pay for and deliver care (http://innovations.cms.gov), there is little agreement on which health care models will best achieve measureable progress on the challenges of cost and quality. Innovating health care systems, such as Kaiser Permanente, Intermountain Health Care, the Mayo Clinic, Geisinger Health System, and Bellin Health, are already taking action to achieve what the Institute for Healthcare Improvement (IHI) calls the Triple Aim. These three imperatives aim to do the following: (1) provide effective, safe, and reliable care to individuals; (2) improve the health of populations by focusing on prevention, wellness, and improved management of chronic disease; and (3) decrease per capita costs. (For an informative discussion on health care system innovation strategies in the United States, the reader is referred to Bisognano & Kenney, 2012).


Among the many options available to promote these goals, one stands out—wider deployment of and expanded practice parameters for advanced practice nurses (APNs). There is a fundamental fit between these national imperatives and the skills and expertise of each APN role. However, there are impediments in the regulatory environment that may place barriers to the full deployment of APNs. In a recent white paper published by the Institute of Medicine (IOM, 2011), The Future of Nursing: Leading Change, Advancing Health, Safriet (2011) noted that there are “conflicting and restrictive state provisions governing [APNs] scope of practice and prescriptive authority … as well as the fragmented and parsimonious state and federal standards for their reimbursement” (p. 2). Numerous examples are cited, in which APNs may not examine and certify patients for worker’s compensation, order treatment for long-term care, or even make declarations of death. Extreme limitations in prescriptive authority exist in many states, as well as constraints for ordering durable medical equipment, physical therapy, or laboratory tests. Although Safriet (2011) has provided a comprehensive review of the many restrictions and limitations on APN practice and offers an array of arguments for reforming these regulatory restrictions, expanding APN practice autonomy and allowing for direct reimbursement, one thing is clear—the ability to demonstrate in a reliable and quantifiable manner that quality and cost-effective care can be consistently delivered by APNs is essential. Not only is this information needed to reform regulatory restrictions, it is needed by health care organizations to promote broader adoption and optimization of APN professional services in their emerging health care delivery models. Finally, this information is needed by the public to help raise awareness that APNs can deliver quality care in cost-effective ways. This means that it is every APN’s responsibility to engage fully in meaningful evaluative activities that will inform all stakeholders, including other APNs, about their effectiveness.


This chapter will introduce basic to intermediate skills for using health care information technology and information management for the purposes of evaluating and improving practice and evaluating and improving outcomes for the patient populations served by APNs. For our purposes here, the terms outcome evaluation and performance improvement will be used interchangeably and may refer to three levels of outcome evaluation: (1) activities that evaluate individual APN practice, such as peer review; (2) activities that evaluate the collective value of all APNs to an organization or population, such as a research or demonstration project; or (3) outcomes in clinical populations served by all providers, including APNs, in an organization or population. Depending on the nature of the outcome evaluation activity and metrics used to monitor performance, these activities may at times overlap and are not always mutually exclusive. For example, a performance indicator that examines the percentage of patients with diabetes who are evaluated annually to determine their hemoglobin A1c (HbA1c) level may be used to evaluate an individual APN’s compliance to a standard of care, or it could be used to illustrate that populations managed by APNs have an acceptable rate of compliance compared to medical providers. This same indicator could also be what may be referred to as provider-agnostic, and used by a health care organization to report compliance to this best practice standard to a broader audience to demonstrate their ability to monitor high-risk populations appropriately in their community or region. Regardless of whether APNs engage in activities to monitor their own practice or the practice of other individual APNs in their organization, it is likely that APNs will also be engaged in the evaluation of quality and financial outcomes for broader populations, such as for a given payer (e.g., the Medicare population). The focus of the chapter will be on the competencies needed to perform outcome evaluation and manage health information using systems that are developing in the United States, although there are many resources used internationally that are also identified. This is an exceedingly complex landscape, with terminology unfamiliar to most clinicians, but it is critical to APN success in the current health care climate.



Regulatory Reporting Initiatives that Drive Performance Improvement


In addition to the regulatory environment that affects APN practice, there is also a dynamic and ever-changing regulatory environment to which health care organizations must respond. In the United States, legislative requirements are being released by the CMS or HHS and posted in the Federal Registry numerous times each year. With each new posting, expanding reporting requirements are imposed on almost every point of service across the health care continuum. Although these reporting requirements are generally presented in the spirit of improving quality and patient safety, requirements for reporting quality data are increasingly being tied to financial reimbursement. Reimbursement for Medicare and Medicaid claims can be reduced for excessive readmissions, hospital-acquired infections and complications, substandard performance in evidence-based best practices, and decreased patient satisfaction scores. In postacute care settings there are emerging reporting requirements to monitor worsening of pressure ulcers, catheter-acquired urinary tract infections (CAUTIs), central line–associated blood stream infections (CLABSIs), and vaccination of health care personnel for influenza. The United States has truly evolved from a pay for reporting culture to one of pay for performance. Table 24-1 provides a summary of these reporting programs, along with a brief description of their impact on Medicare reimbursement.



Because many CMS reporting initiatives reuse the same measures, it is possible for substandard performance in even a single measure to have a number of financial impacts across a health care organization. For example, one measure that looks at the delivery of percutaneous coronary intervention (PCI) within 90 minutes of hospital arrival for acute myocardial infarction (AMI) is used in three reporting programs, including the CMS Hospital Inpatient Quality Program (HIQR), CMS Value-Based Purchasing (VBP) program, and CMS Stage 2 Meaningful Use of the electronic health record (EHR) program for hospitals and critical access hospitals. Failure to collect accurate and complete data, report it to the CMS by designated time frames, and achieve acceptable results in performance for even one required measure can result in sizeable reduction in payment to the hospital from the CMS.


Some measures, such as CLABSI, not only apply to multiple quality reporting programs in a single health care facility, they also apply to multiple care settings across the continuum, including acute care hospitals, cancer care hospitals, ambulatory surgery centers, and long-term care facilities. Thus, failure to comply with reporting requirements has the potential to affect reimbursement across an entire health care system. Additional measurement and reporting requirements for specialized services and health care systems also exist. Box 24-1 illustrates a recent set of measures that are required for health care systems reorganizing as accountable care organizations (ACOs). These measures focus on ambulatory populations and may overlap with several other reporting programs that exist simultaneously across an integrated health care organization, such as the measures required by the Health Resources and Services Administration (HRSA) for federal qualified health centers (see Box 24-2) or those required by providers who care for patients within selected payer niches, such as the Medicaid Adult Quality Measures Program (Box 24-3).



imageBox 24-1


Shared Savings Program for Accountable Care Organizations Quality Measures






*CAHPS asks patients to report on their experiences with a range of health care services at multiple levels of the delivery system. The survey asks about experiences with ambulatory care providers (e.g., health plans, physician offices, home care programs, mental health plans). The survey used to ask about experiences with care delivered in facilities such as hospitals and nursing homes, referred to as the HCAHPS survey. Although similar in nature, CAHPS and HCAHPS use slightly different questions and require specific protocols for survey administration. For additional information, see http://www.cahps.ahrq.gov/surveysguidance.htm.


Adapted from Center for Medicare & Medicaid Services (CMS). (2011). Medicare program; Medicare shared savings program: Accountable care organizations (https://www.federalregister.gov/articles/2011/11/02/2011-27461/medicare-program-medicare-shared-savings-program-accountable-care-organizations#h-68).



imageBox 24-2


Health Resources & Services Administration (HRSA) Uniform Data Set for Clinical and Financial Performance Measures*



Outreach and Quality of Care (%)



• Pregnant women beginning prenatal care in the first trimester


• Children age 2 yr during measurement year, with appropriate immunizations


• Women 21-64 yr of age who received one or more tests to screen for cervical cancer


• Patients 2-17 yr of age who had BMI percentile documentation, counseling for nutrition, and counseling for physical activity during the measurement year


• Patients ≥18 yr of age who had BMI calculated at last visit or within last 6 mo and, if overweight or underweight, had follow-up plan documented


• Patients ≥18 yr of age who were queried about tobacco use one or more times within 24 mo


• Patients ≥18 yr of age who are tobacco users and who received advice to quit smoking or tobacco use


• Patients 5-40 yr of age with a diagnosis of persistent asthma who were prescribed the preferred long-term control medication or an acceptable alternative pharmacologic therapy during the current year




*Required for federally qualified health centers.


Adapted from Health Resources and Services Administration (HRSA). (2011). Clinical and financial performance measures (http://bphc.hrsa.gov/policiesregulationsperformancemeasures/index.html).



imageBox 24-3


CMS Medicaid Adult Quality Reporting Quality Measures








Adapted from Center for Medicare & Medicaid Services (CMS). (2012). Medicaid program: Initial core set of health care quality measures for Medicaid-eligible adults (https://www.federalregister.gov/articles/2012/01/04/2011-33756/medicaid-program-initial-core-set-of-health-care-quality-measures-for-medicaid-eligible-adults#h-17).


In addition to payer-mandated reporting programs, health care organizations also have specific reporting requirements to their accreditation bodies, including The Joint Commission (TJC), Healthcare Facilities Accreditation Program (HFAP), and Det Norske Veritas Healthcare (DNV). It is not unusual for many of the measures required for accreditation to overlap with those reported to the CMS, although these measures typically reflect an all-payer population. What is important to note is that although many of these measures are similar across reporting programs, each program has its own specific requirements for data collection, data quality, and data submission, which must be strictly adhered to in order to ensure proper accreditation, achieve financial incentives, or avoid financial penalties. APNs should become familiar with the regulatory reporting requirements in their organization, recognizing that some measures may merit higher degrees of APN engagement and stewardship to create and sustain optimal results. The National Quality Forum (NQF) Community Tool to Align Measurement is a useful tool for the APN to review so he or she can become familiar with the measures required for his or her practice setting. This tool organizes NQF-endorsed clinical quality measures associated with major national and state reporting initiatives for all practice settings into a single spreadsheet, which can then be sorted by various programs of interest. Hyperlinks are embedded in the spreadsheet so that it is easy to access the Quality Positioning System on the NQF website, on which measure definitions for each metric are maintained (http://www.qualityforum.org/AlignmentTool).


Traditionally, many reporting requirements required manual chart abstraction efforts, along with an element of clinical judgment to determine the correct response to questions about whether or not a particular standard of evidence-based best practice was met. Questions such as “Did the patient get smoking advice or cessation prior to discharge?” are typically abstracted by a review of patient education documentation in the medical record. However, some questions, such as “Was the patient eligible for ACE inhibitors at discharge?” may require a review and synthesis of multiple sources of information in the record, including the history and physical examination, cardiac imaging tests, or medical provider progress notes, before a determination can be made. These types of activities can be time-consuming and often are called into question about their reliability. APNs may be engaged in many aspects of the data collection and analysis activities associated with chart-abstracted measures and should be mindful to avoid data collection activities that are strictly clerical in nature. Additional considerations about the APN role in data abstraction activities will be presented later, in the section on foundational competencies.


Finally, additional regulatory reporting requirements are beginning to emerge in the areas of patient safety, an area in which APNs will likely become engaged. The Patient Safety and Quality Improvement Act of 2005 established a voluntary patient safety event reporting system and guidelines for the establishment of patient safety organizations (Agency for Healthcare Research and Quality [AHRQ], 2005). This act called for the standardization of data used for event reporting based on the common formats established and maintained by AHRQ. Some of the initial risk events being reported include medication errors, patient falls, central line infections, and pressure ulcers. At the time of this writing, the NQF is proposing to include the reporting of readmissions data into patient safety organizations (PSOs) for purposes of shared learning across organizations. Although these emerging reporting requirements have not yet been adopted internationally, they are originally based on an international movement through the World Health Organization (WHO), which adopted a framework for an International Classification for Patient Safety to compare patient safety across disciplines and organizations and examine the roles of system and human factors in patient safety (WHO, 2009). The longitudinal work of the WHO is to develop an international classification system of codes that can be used to track patient safety events and the contributing factors leading up to the adverse event. APNs can play a critical role in identifying potential safety issues and developing priorities and safety solutions across all care delivery settings.



Relevance of Regulatory Reporting to Advanced Practice Nursing Outcomes


The national quality, patient safety, and accreditation reporting requirements are relevant to APN outcome evaluation for several reasons. First, they are of critical interest to the organizations that employ APNs. Many health care organizations are carefully tracking key performance measures that affect their financial bottom line in scorecards or dashboards, which are then communicated to all stakeholders, from clinical units to the board room. In many organizations, financial incentives and annual bonuses for those in strategic and operational leadership roles, as well as other key clinical staff, are based on achieving designated performance thresholds. Organizations monitor their performance against competitive market segments using comparative benchmarking systems so that they can ensure that their performance exceeds that of their peer groups. This is important not only to maintain their reputation within their community and market but also because several CMS programs, specifically the Value-Based Purchasing (VBP) and Hospital Readmission Reduction Programs, base financial incentives on the organization’s ranking across approximately 3000 U.S. hospitals that are competing for incentive dollars in these programs.


The second and more compelling reason why national regulatory reporting initiatives are critical to APN outcome evaluation is because so many of the clinical processes and outcomes reflected in these performance measures are directly sensitive to APN intervention. Thus, active participation in the data collection, data analysis, and resulting performance improvement initiatives provide a rich forum to make the value added associated with the APN contribution highly visible across the organization. In many organizations, key regulatory performance metrics are included in individual provider profiles and integrated into ongoing professional practice evaluation (OPPE) activities. Although the APN has a direct and immediate opportunity to influence outcomes for many of these measures, linking APN to the performance trend is often a challenge, particularly in an acute care hospital environment in which so many providers contribute to the management of a single patient’s care episode. For example, medical orders for a patient with heart failure (HF) who is eligible for angiotensin-converting enzyme (ACE) inhibitors at discharge could be written by the patient’s physician, cardiac specialist, hospitalist, or acute care nurse practitioner (ACNP) who is accountable for overseeing the cardiac medical population. In the absence of a fully adopted computerized physician order entry (CPOE) system, most information or clinical documentation systems do not capture the provider responsible for ordering individual medications or nonprocedural treatments. This often causes great debate and mistrust of the data among providers.


One approach to this limitation used by many organizations is to conduct a quality review on patients who failed to receive a given standard of care. Quality specialists review the medical records for each opportunity for improvement, whereby provider attribution is assigned directly in the quality software and displayed in the provider scorecard. In this type of metric, the desired performance target is zero deficiencies versus 100% compliance. Medical providers with a recurring pattern of deficiencies are then subject to more formal peer review processes within their organizations. Another model for achieving accurate provider attribution is to select four to six records per month randomly for each provider and evaluate whether specific standards of care were met or not met. Values are tracked in a profile and reported in monthly committee meetings. APN provider groups can achieve greater levels of transparency and credibility across their organization by placing key metrics that track compliance to known best practices on department and corporate scorecards and dashboards.



Foundational Competencies in Managing Health Information Technology


At the heart of any performance measurement activity is the ability to collect data and analyze results effectively that can reliably and accurately inform and educate stakeholders about an outcome or process. In health care, these data typically consist of three types of information—clinical, financial, and administrative data. Clinical data, such as a patient’s medication list or a radiology report, are generally found in the EHR (also referred to as the electronic medical record [EMR]). In some cases, clinical data may be found in specialized registries, such as the registry of the Society of Thoracic Surgeons (STS) which collects data on open heart surgical outcomes and related cardiac diseases. Financial data, such as the cost of a given hospital stay or drug, is typically tied to a cost accounting, general ledger, or billing system. Administrative data, such as a patient’s age, gender, or address is typically tied to a patient registration system (often referred to as an admission-discharge-transfer [ADT] system in acute care hospitals). Collectively, the health information systems (HIS) that store these data types are generally referred to as health information technology (HIT), although the term HIT often has broader implications, including servers, cloud-based platforms, networks, clinical data warehouses, mobile applications, and point of care laboratory devices. Minimum informatics competencies are required before one may access the various HIT applications in an organization.


Although APNs directly engage with these technologies at various levels, not all APNs are able to manipulate independently the various HIS needed to compile the data required to evaluate outcomes. More advanced skills from nurse informatics specialists or other expert report writers may be needed to capture necessary information, such as creating a customized report or merging files from multiple databases to assemble the required information. However, stronger informatics competencies in HIT are needed by APNs to design the evaluation strategy, validate the data, and interpret the findings effectively. For APNs prepared at the Doctor of Nursing Practice (DNP) level, the expectation is that the APN is prepared to “apply new knowledge, manage individual and aggregate level information, and assess the efficacy of patient care technology appropriate to a specialized area of practice” (American Association of Colleges of Nursing [AACN], 2006, p. 12). DNP graduates also design, select, and use information systems technology to evaluate programs of care, outcomes of care, and care systems. The expectations for being fluent in the use of technology are reflected in almost every DNP competency (AACN, 2006).



TIGER Competencies for Use of Health Care Information Technology


The Technology Informatics Guiding Education Reform (TIGER) Initiative was formed in 2004 to bring together nursing stakeholders to develop a shared vision, strategies, and specific actions for improving nursing practice, education, and the delivery of patient care through the use of HIT. The TIGER Informatics Competencies Collaborative (TICC) team was formed to develop informatics recommendations for all practicing nurses and graduating nursing students, including APNs. The TIGER competency model consists of three parts—basic computer competencies, information literacy, and information management. It was the ambitious goal of the TIGER Initiative that all three million practicing and graduating nurses in the United States achieve these competencies by January 2013.


The basic computer competency requires the completion of the European Computer Driving License (ECDL) Foundation set of computer competencies. Although at first glance these competencies appear rather basic, they are comprehensive and include concepts relating to hardware, securities, networks, system performance, file compression and file transfer protocols, file management, operating systems, and a wide range of utilities. ECDL certification, already completed by over seven million Europeans, requires more than 30 hours of study and consists of seven modules—concepts of information and communication technology, file management, word processing, spreadsheets, using databases, presentation, and web browsing.


The second competency involves information literacy. Information literacy is critical to incorporating evidence-based practice into nursing practice. The APN must be able to determine what information is needed to assess outcomes of care, identify practice variation, and establish best practices. Furthermore, critical thinking and assessment skills are needed when evaluating or appraising the information and validating the source of the information. There are five components of information literacy that promote competency in the skills necessary to define what information is needed to solve a particular problem and to access, evaluate, and use that information effectively and efficiently.


The third competency involves information management, which is the underlying concept for performance measurement. This involves the process of collecting data, processing data, and presenting and communicating the processed data as meaningful information or knowledge. In addition, APNs must understand and comply with their organization’s Health Insurance Portability and Accountability Act (HIPAA) policies and procedures, which provide for how and when patient protected health information (PHI) may be used and under what circumstances it must be de-identified (meaning the removal of any unique patient identifiers, such as name, birth date, medical record numbers, social security numbers, and other personal information that could potentially identify specific individuals within a data set). Breaches of information, such as an unintentional disclosure of PHI in an unencrypted e-mail or a stolen laptop or device with PHI on it, must be immediately reported to the organization’s compliance officer; this can result in financial fines and legal consequences for the health care organization. Box 24-4 lists resources for obtaining additional information about the TIGER competencies.



imageBox 24-4   Resources for European Computer Driving License Certification, Information Literacy, and Information Management











In addition to the TIGER competencies, APNs engaged in performance improvement activities may also require additional expertise in the information and quality management domains. These include an understanding of the various coding taxonomies and terminology sets used to classify health care data, proficiency with statistical tools, such as statistical process control charts (SPCs), which examine time-trended data, familiarity with benchmark data, data mining methods and analytics, and knowledge about any risk adjustment methodologies that may pertain to the clinical populations being evaluated.


Although organizations such as the IHI and the National Association for Healthcare Quality (NAHQ) can support the APN in achieving competency in many of the skill sets needed for successful outcome evaluation, developing such expertise in graduate programs is a challenge. Academic institutions that prepare APNs must seek alliances with practice environments that not only exemplify or aspire to the best in professional nursing practice, but also are learning organizations that are fully invested in the quality improvement process. Institutional commitments to adopting best practices are essential if health care systems are to keep pace with new developments in performance measurement and outcomes evaluation. In addition, a robust, integrated, and reliable information systems infrastructure in the clinical environment is essential to assist the APN student and graduate to achieve hands-on competencies in informatics and system evaluation.


Although most health care organizations have quality management departments with the expertise to build an outcome evaluation plan and conduct the actual data collection and reporting tasks, the APN should understand these concepts to participate in, and in some cases lead, performance measurement activities actively throughout the full data-information-knowledge continuum. Without these competencies and skill sets, APNs may find that their level of participation is often limited to routine data collection tasks that do not require critical thinking or clinical judgment. APNs should limit participation in direct data collection activities unless it is part of their professional peer review process or if the data collection process is integrated into direct patient care and clinical documentation activities. For example, the minimum data set (MDS) used in long-term care settings to capture quality indicators relating to pressure ulcers, CAUTI and CLABSI, are routinely collected from retrospective data and do not require an APN’s time to abstract records and prepare reports. Rather, APNs should be validating the accuracy of the data, monitoring performance trends, and ensuring that best practices are implemented and fully adopted by the interdisciplinary team. Exemplar 24-1 illustrates a scenario of appropriate APN engagement with data collection activities associated with a professional peer review process for the purposes of outcome evaluation and performance improvement.



imageExemplar 24-1   Walgreens Take Care ClinicsSM Make Advanced Practice Nursing Outcomes Transparent to the World*


Walgreens Take Care Clinics has created an emerging market segment in health care delivery. The care provided is acute and episodic, family-oriented care for patients older than 18 months. Although not positioned like an emergency or urgent care clinic, in which full laboratory and imaging resources are readily available, the care provided in the Take Care Clinics help patients without immediate access to primary care providers while also providing preventive services, such as immunizations. Common complaints typically addressed with this service include upper respiratory infections (URIs), ear infections, minor sprains and injuries, school physicals, vaccinations, and screening and counseling services for hypertension, hyperlipidemia, and diabetes. More urgent conditions, such as sudden onset chest pain, active bleeding, or trauma are redirected to the nearest emergency care center. Patients can preschedule clinic visits online or walk in during hours of operation, which include extended evening and weekend hours. Clinics are located directly inside select Walgreens stores across the United States and, as of this writing, are staffed by over 1400 board-certified family nurse practitioners (FNPs).


APNs who practice in a Walgreens Take Care Clinic typically care for patients in an autonomous and technology-driven environment. Patients register at a touch screen kiosk when they arrive and services and pricing are visible via a liquid crystal display (LCD) screen outside the clinic. Patients are then seen in an examining room, which is generally located next to the pharmacy. During the visit, the APN will interview the patient, entering information into a proprietary EHR designed expressly for Walgreens, with input from APN end users. This EHR contains key information about the patient’s health status and prompts the APN to inquire about selected data elements necessary to compute various health care quality outcomes. It also tracks any prescribed medications and generates discharge instructions, which are printed out and provided to the patients prior to their departure from the examination room, allowing the patient time and privacy to review instructions with the APN. Finally, patients are invited to take a brief satisfaction survey in the kiosk outside the examination room following their visit, thus ensuring feedback immediately after the visit.


As part of the Walgreens quality improvement program, Take Care Clinic uses a tool known as HEDIS (health care effectiveness data and information set). HEDIS is a set of health care quality standards developed by the National Committee for Quality Assurance (NCQA). HEDIS is used by hospitals, physician offices, and clinics across the country to measure the quality of care they provide. Three performance measures are publicly reported at http://takecarehealth.com/Quality_And_Patient.aspx and include the following:



Every month, each APN is engaged in a professional peer review process whereby they receive randomly assigned electronic charts to review against best practice standards and quality documentation. Patient identifiers are stripped from the record, as is any information that will identify the individual APN who delivered the care. APNs then review their assigned records and enter their feedback into the computerized peer review application. In some cases, recommendations are offered to assist the growth and development of a fellow practitioner. For example, in one clinical review of a pediatric patient who presented with a URI, as well as childhood eczema, the reviewer offered a comment that it might be helpful to offer the family specific dietary counseling and skin care advice for improved management of the eczema as a comorbid condition. This recommendation later became part of a larger quality improvement strategy whereby patient educational materials on diet and skin care for pediatric eczema were developed and made available for use by all APNs across the Walgreens network.


On completion of monthly peer review activities, results of the audits are returned to the APN who completed the care; the identities of the reviewers are not disclosed. APNs may then review and validate their scores and feedback. Although occurring very rarely, there is a process for an APN to challenge their review findings by meeting with their local area APN manager and, if needed, the medical provider assigned to their practice. In addition, each APN has the ability to participate in monthly grand rounds, during which interesting and challenging cases are presented and discussed. Finally, all clinic quality and patient satisfaction scores are reported and discussed in quarterly market meetings, in which attendance is required for all Walgreens APN employees nationally. Quality scores for the HEDIS measures are consistently outstanding and significantly exceed published national benchmarks, thus meeting best practice standards in the treatment of URI, bronchitis, and pharyngitis in adult and pediatric populations. Patient satisfaction is reported by region using the Gallup patient engagement score (1 to 5), in which responses typically average in the 4.4 to 4.78 range for all regions across the United States and are viewable for patients by region online.


*The author wishes to thank Walgreens and Sandra Ryan, Chief Nurse Practitioner Officer for Walgreens Take Care Clinics, for their generous time and support in describing the quality improvement process used by the APN practices at Walgreens.



Coding Taxonomies and Classification Systems


As noted, APNs must understand the data-information-knowledge continuum and be able to articulate the type of data that they require to meet their information needs most effectively. In this regard, it is necessary for APNs to understand how various coding taxonomies and terminology sets are used in health information management for generating claims, and how this information can be leveraged for purposes of outcome evaluation. For inpatient settings, this would include concepts of the International Classification of Diseases (ICD; for versions 9 and 10; ICD-9 and ICD-10) codes for diagnoses and procedures. ICD codes are alphanumeric designations given to almost every diagnosis, description of symptoms, and cause of death attributed to humans and are used worldwide.


In addition, APNs practicing in acute care hospital settings must understand that for every claim there is a single principle ICD diagnosis code that indicates the primary reason for a patient’s admission to the hospital. In addition, there may be multiple secondary ICD diagnosis codes that represent comorbid conditions that were present at the time of admission or conditions and complications that were acquired during the hospital stay. ICD procedure codes also have a designated principle procedure, which is typically the initial major procedure during the admission or care encounter. Many U.S. hospitals code as many as 25 to 50 secondary ICD-9 diagnoses and procedure codes in the medical claim; this number is expected to rise significantly once the transition from ICD-9-CM to ICD-10-CM is complete in October 2014. In addition, the ICD diagnosis status flag of present on admission (POA) or not present on admission (NPOA) can be used to differentiate between hospital-acquired conditions and those acquired prior to hospitalization. This is useful when evaluating phenomenon such as hospital-acquired pneumonia, pressure ulcers, acute renal failure, and sepsis. The APN should become familiar with the coding practices in their practice settings, because not all countries or organizations have adopted the use of the POA and NPOA status flags.


APNs practicing in the US should also have a strong working knowledge of the Medicare Severity Diagnosis-Related Groups (MS-DRGs). They should understand how clinical documentation by APN and medical providers directly affect MS-DRG assignments and ultimately reimbursement. Many U.S. health care organizations are actively implementing clinical documentation improvement (CDI) programs in their institutions for purposes of coaching and training medical providers and APNs in improving their documentation skills for greater precision and accuracy in medical records coding and billing. Greater detail in the medical record can potentially lead to higher reimbursement and fewer denials and audits from payers, which are prevalent in the U.S. health care payment system. APNs should expect to have some level of inspection and chart review built into their credentialing process that reflects CDI initiatives in their organization, especially if they are practicing in an acute care setting.


Finally, it may be useful for APNs to understand other types of coding taxonomies and terminology sets that are available for purposes of quality reporting and research. Table 24-2 summarizes several common taxonomies used in inpatient and ambulatory settings.



imageTABLE 24-2


Coding Taxonomies and Terminology Sets Commonly Used in Outcomes Evaluation



















































Coding Taxonomy Description Website
ICD-9-CM: ICD, version 9, clinical modification diagnosis codes Approximately 13,000 codes three to five characters in length
Used primarily in the United States and United Arab Emirates (UAE) to classify diagnoses for inpatient hospital claims
Coding set lacks detail and laterality.
Limited space for adding new codes
United States targeted to sunset ICD-9 diagnosis codes October 1, 2014.		 www.cms.gov/ICD9
ICD-9 procedure codes: ICD, version 9, procedure codes Approximately 3,000 codes three or four characters in length
Used primarily in the United States to classify diagnoses for inpatient hospital claims
Coding set lacks detail and laterality
Lacks precision to define procedures adequately. Based on outdated technology
United States targeted to sunset ICD-9 procedure codes October 1, 2014.		 www.cms.gov/ICD9
ICD-10-CM diagnosis codes: ICD, version 10, clinical modification diagnosis codes Approximately 68,000 available codes three to seven alphanumeric characters in length
Used by most countries worldwide (United States targeted for transition in 2014) to classify diagnoses for inpatient hospital claims
Coding set is very specific and contains laterality.
Flexible for adding new codes		 www.cms.gov/ICD10
ICD-10 procedure codes: ICD, version 10, procedure codes Approximately 87,000 available codes seven alphanumeric characters in length
Used by most countries worldwide except the United States and UAE (United States targeted for transition in 2013) to classify procedures for inpatient hospital claims
Coding set very specific, provides for precisely defined procedures and laterality.		 www.cms.gov/ICD10
CPT (current procedural terminology) Approximately 7800 available codes for reporting medical, surgical, and diagnostic services in outpatient and office settings, as well as acute care emergency settings, ambulatory surgery, and inpatient procedures done in some hospitals outside the United States.
CPT is a registered trademark of the American Medical Association and is considered a proprietary terminology set requiring licensure before using inside an HIT application.
New codes are released each October.		 www.ama-assn.org/med-sci/cpt
HCPCS: Healthcare Common Procedure Coding System There are two types of HCPCS codes. Level 1 HCPCS codes are identical to CPT codes used for reporting services and procedures in outpatient and office settings to Medicare, Medicaid, and private health insurers. Level II HCPCS codes are used by medical suppliers other than physicians, such as ambulance services or durable medical equipment. www.cms.gov/HCPCSReleaseCodeSets
SNOMED-CT Complex and highly hierarchical collection of over a million codes and medical terms that describe diseases, procedures, symptoms, findings, and more
Developed by the College of American Pathologists and the National Health Service (Britain)
Used extensively throughout the world, SNOMED-CT codes help organize content in the EHR and cross-walk to other terminologies such as ICD-9, ICD-10, and LOINC.		 www.snomed.org
LOINC Universal standard containing 58,000 observation terms for identifying medical laboratory tests and clinical observations, as well as nursing diagnosis, nursing interventions, outcomes classification, and patient care data sets
Originally developed in the United States in 1994; international adoption expanding rapidly		 http://loinc.org
RxNorm Standardized nomenclature for clinical drugs produced by the National Library of Medicine. This data set is updated monthly to stay abreast of the rapidly changing pharmaceutical industry. It contains links between national drug codes, which are used widely in EHRs and e-prescribing systems. http://www.nlm.nih.gov/research/umls/rxnorm/docs/index.html
MS-DRGs Each inpatient hospital stay in the United States is assigned one of over 750 MS-DRG codes, which are used for billing to Medicare and other payers. Codes are derived from ICD diagnoses and procedure codes. Many conditions are split into one, two, or three MS-DRGs based on whether any one of the secondary diagnoses has been categorized as a major complication or comorbidity (MCC), a CC, or no CC, and are weighted accordingly to reflect severity and reimbursement. Note that a separate MS-DRG code set for long-term care is used (MS-LTC-DRG). Additional coding sets apply to other areas of care (e.g., resource utilization groups [RUGs] apply to skilled nursing and rehabilitation stays). www.cms.hhs.gov/AcuteInpatientPPS/FFD/list.asp
3M APR DRGs: (All Patient Refined Diagnosis Related Groups) Used predominately for illness severity and risk of mortality adjustments, this proprietary coding set is commonly used to disseminate comparative performance data for hospitals and providers. APR-DRGs are derived from ICD codes; inpatients are assigned one APR-DRG code, along with a severity of illness code (1-4) and a risk of mortality code (1-4). Although APR DRGs are used internationally, 3M also supports international refined (IR-DRGs), which apply to inpatient and outpatient populations. Additional 3M methods are available to assess and forecast longitudinal resource consumption for designated populations. http://solutions.3m.com/wps/portal/3M

Data such as ICD or Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT) codes that have alphanumeric identifiers that can be easily queried from a database are termed discrete or structured data. Data containing dictated sentences and phrases, such as radiology reports, operative summaries, or history and physical dictations are termed nondiscrete or unstructured. Compiling and analyzing unstructured data is typically more challenging. Unless the organization has technology with a sophisticated reporting system containing a tool for natural language processing (NLP), with a well-developed and tested rules engine for discriminating between patients with active pneumonia and those without (e.g., the phrases “pneumonia ruled out” or “no signs of pneumonia” should not be included in a list of active pneumonia patients), obtaining this type of data may yield too many false-positives to be a reliable and valid screening tool.


Although it is not necessary for the APN to understand the precision behind the coding processes, it is highly recommended that APNs spend time shadowing with a medical records coder (also referred to as a health information management specialist) to understand the timing and manner in which the various terminology sets are used. This is important to outcomes evaluation because some types of data are more useful than others, even though they represent similar concepts. For example, the phenomenon of pneumonia could be captured from the claim (using an ICD-9 diagnosis or CPT-4 code), laboratory culture and sensitivity report (using Logical Observation Identifiers Names and Codes [LOINC] and RxNorm codes), problem list in the EHR (using SNOMED-CT codes), chest imaging report (using natural language processing), or a dictated history and physical report (requiring manual chart abstraction). Although each source of information in the EHR may be correct, the APN may have to determine the best source of information to address the nature of the inquiry in the most timely, efficient, and accurate manner. Exemplar 24-2 illustrates the critical evaluation of the various data types that an APN would conduct when planning to deploy a new strategy to improve care, as well as the collaborative process with quality and informatics specialists that will benefit the APN in the outcome evaluation efforts.



imageExemplar 24-2   Evaluating Data Types When Using Health Information Technology to Implement Practice Change


An APN working in a 400-bed, acute care setting needs to identify all inpatients with an active diagnosis of pneumonia to ensure that all best practice interventions have been implemented in a timely and appropriate manner. Although the performance in these areas is retrospectively monitored and reported periodically by the quality management department, the APN wishes to engage in the process concurrently to influence outcomes for this population. The APN’s intervention is to perform clinical rounds several times a day on this population to ensure optimal delivery of best practice standards. These include antibiotic timeliness, appropriateness of antibiotic selection in intensive care unit (ICU) and non-ICU patients, smoking cessation advice and counseling, and influenza and pneumococcal vaccinations. The APN wants to leverage the hospital’s information systems to create a real-time alert notification system to identify and track patients with pneumonia immediately on any nursing unit and then monitor performance trends to reflect the impact of her interventions on this population.


The APN recognizes that there are multiple taxonomies and methodologies that could be used to identify pneumonia patients in her organization and begins to inventory the pros and cons of each data type and source to create the best technology solution for her requirements. ICD-9 diagnosis codes are a typical taxonomy for identifying patients with pneumonia. Although these codes are readily available, reliable and robust population identifiers in other types of outcome evaluation measures used in her organization, such as length of stay, mortality, and readmissions, this approach would not be optimal because ICD-9 codes are typically not assigned until after the patient is discharged and the medical record is coded for the claim. Using this type of data element is too retrospective to be able to identify patients with pneumonia on a real-time basis.


The APN next considers a more concurrent coding methodology, which is based on the active problem lists in the EHR. In collaboration with a health information management specialist, a list of problem types and their corresponding SNOMED-CT codes is established, which can be used to identify patients with an active diagnosis of pneumonia. However, this approach is ruled out because medical provider adoption of the new EHR has not yet fully expanded to the critical care units at her hospital. In addition, she has observed that many medical providers in the organization do not update the problem list in the EHR until the patient is being discharged, which makes a real-time query unreliable.


The final option that the APN considers to create automated alerts is the use of radiology and laboratory reports with the words containing pneumonia or other related terms. In discussing this option with a nursing informatics specialist at her organization, she learns that the EHR has been fully implemented to report laboratory results for all clinical areas in the hospital and that this includes microbiology results. Using the LOINC (Logical Observation Identifiers Names and Codes) coding terminology set, patients with pneumonia can be identified as soon as the laboratory result transaction is received in the EHR. In addition, she learns of a pilot project in the Quality Department in which cardiac catheterization reports are being processed by an NLP engine to capture cardiac registry information. She asks about the potential to expand this pilot to include radiology reports to identify pneumonia patients. The Quality Department agrees to expand the project scope with the help of the APN to begin formulating a list of phrases that will be used to identify pneumonia positively in a radiology report. The APN collaborates with the medical director of radiology to create the final list of terms to be used in the pilot.


After 3 months of implementing the practice change, the APN is able to demonstrate that performance in five best practice indicators for patients with pneumonia is almost 100% across the organization. She tracks the compliance rates in an SPC chart, which demonstrates a statistically significant favorable change in the process. Furthermore, she is able to use the organization’s value-based purchasing calculator to demonstrate a financial contribution to the hospital’s Medicare reimbursement program of almost $68,000 simply by improving performance. The APN plans to make a formal recommendation to expand similar APN interventions to other key populations influenced by so-called pay for performance initiatives, including cardiac, medical, orthopedics, and stroke. Thus, the APN is able to materially demonstrate the value-added benefit of APN clinical interventions using financial and quality analytics.

Related posts:

  1. Role Development of the Advanced Practice Nurse
  2. The Acute Care Nurse Practitioner
  3. Leadership
  4. Evidence-Based Practice

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Oct 19, 2016 | Posted by in NURSING | Comments Off on Using Health Care Information Technology to Evaluate and Improve Performance and Patient Outcomes

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