Use of Guidelines to Enhance Quality of Care
Clinical practice guidelines can help enhance quality by synthesizing available research evidence and delineating recommendations for care on the basis of the available evidence. In some circumstances, practice guideline recommendations will be appropriate to use in developing quality measures. Guideline statements can also be used in other ways, such as educational activities or electronic decision support, to enhance the quality of care that patients receive. Furthermore, when availability of services is a major barrier to implementing guideline recommendations, improved tracking of service availability and program development initiatives may need to be implemented by health organizations, health insurance plans, federal or state agencies, or other regulatory programs.
Typically, guideline recommendations that are chosen for development into quality measures will advance one or more aims of the Institute of Medicine’s (2001) report Crossing the Quality Chasm by facilitating care that is safe, effective, patient-centered, timely, efficient, and equitable. To achieve these aims, quality measures (Watkins et al. 2015) are needed that span the continuum of care (e.g., prevention, screening, assessment, treatment, continuing care), address the different levels of the health system hierarchy (e.g., system-wide, organization, program/department, individual clinicians), and include measures of different types (e.g., process, outcome, patient-centered experience). Emphasis is also needed on factors that influence the dissemination and adoption of evidence-based practices (Drake et al. 2008; Greenhalgh et al. 2004; Horvitz-Lennon et al. 2009a).
Often, quality measures will focus on gaps in care or on care processes and outcomes that have significant variability across specialties, health care settings, geographical areas, or patients’ demographic characteristics. Administrative databases, registries, and data from electronic health record (EHR) systems can help to identify gaps in care and key domains that would benefit from performance improvements (Acevedo et al. 2015; Patel et al. 2015; Watkins et al. 2016). Nevertheless, for some guideline statements, evidence of practice gaps or variability will be based on anecdotal observations if the typical practices of psychiatrists and other health professionals are unknown. Variability in the use of guideline-recommended approaches may reflect appropriate differences that are tailored to the patient’s preferences, treatment of co-occurring illnesses, or other clinical circumstances that may not have been studied in the available research. On the other hand, variability may indicate a need to strengthen clinician knowledge or to address other barriers to adopting best practices (Drake et al. 2008; Greenhalgh et al. 2004; Horvitz-Lennon et al. 2009a). When performance is compared among organizations, variability may reflect a need for quality improvement initiatives to improve overall outcomes but could also reflect case-mix differences such as socioeconomic factors or the prevalence of co-occurring illnesses.
Conceptually, quality measures can be developed for purposes of accountability, for internal or health system–based quality improvement, or both. Accountability measures require clinicians to report their rate of performance of a specified process, intermediate outcome, or outcome in a specified group of patients. Because these data are used to determine financial incentives or penalties based on performance, accountability measures must be scientifically validated, have a strong evidence base, fill gaps in care, and be broadly relevant and meaningful to patients, clinicians, and policy makers. Development of such measures is complex and requires development of the measure specification and pilot testing (Center for Health Policy/Center for Primary Care and Outcomes Research and Battelle Memorial Institute 2011; Fernandes-Taylor and Harris 2012; Iyer et al. 2016; Pincus et al. 2016; Watkins et al. 2011). The purpose of the measure specification is to create detailed, clearly written, and precise instructions on the calculation of the measure so that, when implemented, the measure will be consistent, reliable, and effective in addressing quality in a specific target population (Centers for Medicare and Medicaid Services 2023). In contrast, internal or health system–based quality improvement measures are typically designed by and for individual providers, health systems, or payers. They typically focus on measurements that can suggest ways for clinicians or administrators to improve efficiency and delivery of services within a particular setting. Internal or health system–based quality improvement programs may or may not link performance with payment, and, in general, these measures are not subject to strict testing and validation requirements.
Regardless of the purpose of the quality measure, it must be possible to define the applicable patient group (i.e., the denominator) and the clinical action or outcome of interest that is measured (i.e., the numerator) in validated, clear, and quantifiable terms. The measure also needs to be feasible. More specifically, the health system’s or clinician’s performance on the measure must be readily ascertained from chart review, patient-reported outcome measures, registries, or administrative data. In addition, use of the measure should yield improvements in quality of care to justify any clinician burden (e.g., documentation burden) or related administrative costs (e.g., for manual extraction of data from charts, for modifications of EHRs to capture required data elements).
Documentation of quality measures can be challenging, and, depending on the practice setting, can pose practical barriers to meaningful interpretation of quality measures based on guideline recommendations. For example, when recommendations relate to patient assessment or treatment selection, clinical judgment may need to be used to determine whether the clinician has addressed the factors that merit emphasis for an individual patient. In other circumstances, standardized instruments can facilitate quality measurement reporting, but it is difficult to assess the appropriateness of clinical judgment in a validated, standardized manner. Furthermore, utilization of standardized assessments remains low (Fortney et al. 2017), and clinical findings are not routinely documented in a standardized format. Many clinicians appropriately use free text prose to describe symptoms, response to treatment, discussions with family, plans of treatment, and other aspects of care and clinical decision-making. Reviewing these free text records for measurement purposes would be impractical, and it would be difficult to hold clinicians accountable to such measures without advances in natural language processing technology and further increases in EHR use among mental health professionals.
Possible unintended consequences of any measures would also need to be addressed in testing the measure specifications within a variety of practice settings. For example, in many health care systems, multiple clinicians are involved in the care of a patient, and it is misleading if performance on the measure is attributed to the performance of a single clinician or group of clinicians. As another challenge, if the measure specification requires precise wording for the measure to be met, clinicians may begin to document using standardized language that does not accurately reflect what has occurred in practice. If multiple discrete fields are used to capture information, data will be easily retrievable and reportable, but oversimplification is a possible unintended consequence of measurement, and documentation burden is likely to be high (Johnson et al. 2021). Just as guideline developers must balance the benefits and harms of a particular guideline recommendation, developers of performance measures must weigh the potential benefits, burdens, and unintended consequences of optimizing quality measure design and testing.