NSG 601 Provide examples of a structural measure, a process measure, and an outcome measure

NSG 601 Provide examples of a structural measure, a process measure, and an outcome measure

NSG 601 Provide examples of a structural measure, a process measure, and an outcome measure

Quality measurement in the healthcare setting usually takes center stage in every effort to improve quality and offer better patient services. The Donabedian model has been widely used as a guiding light in quality measurement. The model asserts that the measures that are applied in assessing and comparing the health care organization’s quality are categorized as either outcome, process, or structure measures (Tossaint-Schoenmakers et al., 2021). The strength of the model is that it offers a common language that helps describe how a healthcare organization can measure quality.

Process, Outcome, and Structure Measures

            The three types of measures can all be used in any healthcare setting since they are integral to quality improvement. The structural measures refer to those measures that reflect a facility’s systems and capacity to offer high-quality care. On the other hand, process measure indicates what a facility undertakes to maintain or improve health for those diagnosed with various conditions or for healthy individuals (Endeshaw, 2020). In addition, outcome measure reflects an intervention or health care service’s impact on the patient’s health status.

An example of structure measure in a psychiatry setting is the availability of mental health specialists in the care setting (Kilbourne et al., 2018). This measure indicates whether the mental health patients reporting to the clinic will find enough mental health specialists to attend to their needs. One of the associated goals for this measure is to have an adequate number of mental health specialists for better and quality mental health services. An example of a process measure is the reception of the right dosage of psychotherapy. This measure refers to the facility’s capability to provide the right psychotherapy dosage as supported by evidence-based practice. One of the goals associated with this measure is to offer safe and quality mental health treatments by using evidence-based practice to offer the right dosage. In addition, an example of an outcome measure is the improvement and recovery of the symptoms, for instance, symptoms of depression as assessed by valid instruments (Kilbourne et al., 2018). One of the goals associated with the measure is to help patients improve their clinical outcomes through evidence-based management of the symptoms.

Goal Setting and Benchmark.

The setting of quality goals in a healthcare facility is the responsibility of the organization executives in collaboration with various departmental and unit leaders (Hersh et al., 2017). The collaborative goal setting ensures that the goals are aligned with the organization’s needs, employees’ capabilities, and patient needs; the unit leaders play a vital role in reporting what happens in the units for better goal setting. These goals usually have benchmarks for comparison. For instance, when hospital readmission rates are used as a quality measure, the rates observed can be compared to the state level or the Federal data on the same. In addition, the rates can be compared with those of similar psychiatric facilities.

The benchmark data plays a critical role in quality improvement (Hersh et al., 2017). By carefully studying the benchmark data, our organization can identify areas where our facility is falling short and those where the facility is performing well. The organization then strategizes and formulates various methods to improve the areas with poor performances. The plan is to give a time frame and smart goals to be achieved in efforts to correct the poor areas. After the set time frame has elapsed, the organization can therefore compare the newly generated data to the benchmark and plan as appropriate. The benchmark data also helps the organization to keep on improving on the quality measures already doing well.

Informatics and Quality Improvement

Informatics is one technological invention that has changed the healthcare landscape and quality forever (Rahimi et

NSG 601 Provide examples of a structural measure, a process measure, and an outcome measure
NSG 601 Provide examples of a structural measure, a process measure, and an outcome measure

al., 2018). Informatics can be used as part of the process improvement initiative in my workplace. For instance, the psychiatric department usually handles many psychiatric patients throughout the year; hence, robust care coordination is needed. Informatics is used in coordinating care for psychiatric patients by relaying information from the pharmacy, therapists, and physicians, among others. With the use of informatics, us psychiatric nurses can deliver the necessary information to the patients to improve the outcomes and increase patient satisfaction.

 

Quality improvement graphs

(Extracted from Baum et al., 2018)

References

Baum, R. A., Manda, D., Brown, C. M., Anzeljc, S. A., King, M. A., & Duby, J. (2018). A learning collaborative approach to improve mental health service delivery in pediatric primary care. Pediatric quality & safety3(6). http://dx.doi.org/10.1097/pq9.0000000000000119

Endeshaw, B. (2020). Healthcare service quality-measurement models: a review. Journal of Health Research. https://doi.org/10.1108/JHR-07-2019-0152 (Links to an external site.).

Hersh, R. G., Caligor, E., & Yeomans, F. E. (2017). Fundamentals of transference-focused psychotherapy: Applications in psychiatric and medical settings. Springer.

Kilbourne, A. M., Beck, K., Spaeth‐Rublee, B., Ramanuj, P., O’Brien, R. W., Tomoyasu, N., & Pincus, H. A. (2018). Measuring and improving the quality of mental health care: a global perspective. World psychiatry17(1), 30-38. https://dx.doi.org/10.1002%2Fwps.20482 (Links to an external site.).

Rahimi, B., Nadri, H., Afshar, H. L., & Timpka, T. (2018). A systematic review of the technology acceptance model in health informatics. Applied clinical informatics9(03), 604-634. DOI: 10.1055/s-0038-1668091

Tossaint-Schoenmakers, R., Versluis, A., Chavannes, N., Talboom-Kamp, E., & Kasteleyn, M. (2021). The Challenge of Integrating eHealth Into Health Care: Systematic Literature Review of the Donabedian Model of Structure, Process, and Outcome. Journal of medical Internet research23(5), e27180 https://doi.org/10.2196/27180

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There are multiple ways to provide information on measures that effect outcome. They range from identifying goals regarding updates on hospital acquired infections such as catheter acquired urinary tract infections (CAUTI) to ventilator assisted pneumonia (VAP).This type of goal is determined by government organizations that are attempting to lower patient care problems that increase the price of health care and lead to further intervention and mortality.  Measures are identifiable following the completing of interventions and provide metrics that determine if a change was successful (Dang & Dearholt, 2018).

Outcome measures indicate the correlation between interventions and  their outcomes while adjusting for variables outside a provider’s control (Finkelman, 2021). Variables include age, race, and ethnicity. Outcome measures in the workplace provide information that can or can not alter outcomes. An institution can improve patient care by accepting such variables and eliminate them as a cause and effect. Table 1 presented by Jember et al.(2018), identifies variable information that contributes to medication errors, but can not be changed. This data provides organizations the information to determine the variables to medication errors. While this is completely informational, it is relevant to collect and assist in the beginning process of determining reasons for medication error.

Process measures provide information on patient care activities that are initiated by provider to maintain health (Finkelman, 2021). This is relevant to understanding the means presented by providers to protect their patients. Process measures include such things as hand washing and  the percentage of preventive measures used. Identifying this information provides data to assist in determining needs for quality improvement (Dang & Dearholt, 2018). The data collection is determined by the organization and is compared to the same measures as other organization to improve patient care with identifiable factors. Table 2 presents process measures concerning services available in multiple facilities (Leslie et al., 2017). This provides important information to patients looking for specialty services.

Structure measures indicate the attributes of specific systems (Dang & Dearholt, 2018). This information allows patients to make an informed decision based on serevices needed and provided at an organization. Structure measures allow for an informed choice for high-quality care (Finkelman, 2021). While this provides no benchmark comparison, it does allow comparison on needed services. There are numerous sites available to assist in providing individualized assistance to select physicians and hospitals. Hospital organizations and insurance companies provide such information on their specific websites. However, there are information sites like Healthgrades (www.healthgrades.com (Links to an external site.)) that provide a wealth of information.

 

Informatics assist in providing quick access to tools that serve as an assessment or evaluation of needs. The Braden Skin Assessment identifies high risk patients that need appropriate interventions. Evidence based practice indicates that electronic health records (EHR) reduces errors by simplifying the evaluation of needs (Brown et al., 2020). This leads to improved workload, time management, and patient care.

 

 

                                            References

 

Brown, J., Pope, N., Bosch, A. M., Mason, J, & Morgan, A. (2020). Issues affecting nurses’ capability to use digital technology at work: An integrative review. Journal of Clinical Nursing, 29, 2801-2819.  https://doi.org/10.1111/John.15321 (Links to an external site.)

Dang, D. & Dearholt S. lol.  (2018). John Hopkins nursing evidence-based practice:  Model and guidelines (3rd ed.). Sigma Theta Tau International

Finkelman, A. (2021). Quality improvement: A guide for integration in nursing (2nd ed.). Jones & Bartlett Learning

Jember, A., Hailu, M., Messele, A., Demeter, T., & Hassen, M. (2018). Proportion of medication error reporting and associated factors among nurses: A cross-sectional study. BMC Nursing, 17.

Leslie, H.H., Sun, Z., & Kursk, Me. E. (2017). Association between infrastructure and observed quality of care in 4 healthcare services: A cross-sectional study of 4,300 facilities in 8 countries. PLOS. Medicine.Https://doi.org/10.1371/journal.pmed.1002464