The problem which was observable in the past working environment, and, to some extent, can still be traced today, is the inconsistency in the measurement of clinical outcomes. The initial core of the problem was the lack of general understanding of the measurement process and scarcity of theoretical basis which could be used to produce a meaningful framework for clinical outcome management, but the situation has changed.
The organization has since taken advantage of the materials published on the subject, primarily the seminal work by Donabedian, who has laid the foundations of the ” structure – process – outcome” approach (Moore, Lavoie, Bourgeois, & Lapointe, 2015). However, while the general concept has helped to gain an overview of the subject, the associated changes have lacked several subtle details which would be beneficial for a more comprehensive assessment.
The solution chosen by the organization consists of three basic components. The structure in the context of healthcare is the credentials of the establishment, such as the certificates issued by the external controlling entity. The process consists of actions performed by the health care providers, ranging from physical examinations to surgeries to psychiatric interventions. The combination of the two components leads to the outcome – an observable and measurable results, such as decreased mortality, improved time of hospitalization, and a higher level of patient satisfaction.
The framework chosen to perform the measurement and analyze the outcome was the Health Plan Employer Data Information Set (HEDIS), which has the advantage of being tested in various working environments. While certain evidence exists of its low flexibility and adaptability to different settings and areas, such as its incapability to produce valid results for the patients with asthma (Farber & Schatz, 2007), it has several advantages.
First, it is a relatively in-depth system, which includes the data from several marginal areas and producing meaningful data not only for the clinicians but also for employers and stakeholders from the administrative sector. Second, it has a rigid set of rules and definitions, making it a good candidate for usage by the wide variety of healthcare providers without the possibility of uncertainty via incorrect usage. Third, a growing body of evidence confirms the validity of results obtained through the use of HEDIS as well as their applicability for further quantitative analysis (Bremer, Scholle, Keyser, Knox-Houtsinger, & Pincus, 2008).
The change was implemented in two broad phases. The first phase consisted mostly of staff training and education. First, the relevance of the existence of an easily approachable and measurable system of evaluation was explained to the stakeholders. For healthcare providers, the emphasis was made on the better understanding of the efficiency of the process and the possibility of more effective interventions, the researchers were provided with the extended data sets, the investors gained better control of the financial performance of the establishment, and the patients were granted an opportunity to influence the quality of healthcare and establish better communication with clinicians.
The majority of this phase has taken the form of the educational events, except the patient segment, which was informed primarily via printed media, such as explanatory posters and handouts. Immediately after the explanatory part, the staff directly involved in data gathering was trained to utilize the HEDIS framework. In the second phase, the small-scale collection of data commenced to verify the validity of the system and timely detect and correct the inconsistencies. The change process was overseen by the dedicated committee, which assigned the additional training sessions or improved the faulty elements of the set upon detection.
However, one important detail notably lacked in the process. The assessment of the clinical outcomes did not take patient reports into account. While the patients were encouraged to share their experience, the questionnaires and interviews focused more on their satisfaction with the process rather than on the report of its clinical success. The reason for such direction was the attempt to maintain scientific rigor and rely on the falsifiable data rather than on the reports prone to bias and personal perception.
However, while such integrity certainly was beneficial, the recent research has suggested the improvements of including the patient-reported outcomes (PROs) into the final assessment. A study by Van der Wees et al. (2014) has suggested that such data stratum could not only improve the understanding of the individual cases but provide material for aggregated analysis of the comparative effectiveness. Of even greater importance are the possibilities for the insight into the dynamics of customer perception useful for the value-based payment system, which currently gains in popularity in the US (Van der Wees et al., 2014).
While not a critical detail by itself, it still contains certain risks if not utilized. As was mentioned before, the HEDIS does not provide a complete picture in every field and suits better for some than for the others. As a result, the assessment of different departments and processes would have uneven validity, which would introduce uncertainty into the final results. This would be noticeable primarily for the investors, who would be given a somewhat distorted picture of the reasons behind inconsistencies of the organization’s sustainability, and for the patients, who would not be able to communicate all of their concerns to the healthcare providers. Thus, the matter of including PROs to the data is recommended.
Bremer, R. W., Scholle, S., Keyser, D. J., Knox-Houtsinger, J., & Pincus, H. A. (2008). Pay for Performance in Behavioral Health. Psychiatric Services, 59(12), 1419-1429.
Farber, H. J., & Schatz, M. (2007). Health Plan Employer Data and Information Set (HEDIS®) Criteria to Determine the Quality of Asthma Care in Children. Disease Management & Health Outcomes, 15(5), 279-287.
Moore, L., Lavoie, A., Bourgeois, G., & Lapointe, J. (2015). Donabedian’s structure-process-outcome quality of care model: Validation in an integrated trauma system. Journal of Trauma and Acute Care Surgery, 78(6), 1168-1175.
Van Der Wees, P. J., Nijhuis-Van Der Sanden, M. W., Ayanian, J. Z., Black, N., Westert, G. P., & Schneider, E. C. (2014). Integrating the use of patient-reported outcomes for both clinical practice and performance measurement: views of experts from 3 countries. The Milbank Quarterly, 92(4), 754-775.