Workflow Concepts and Staffing Models Sample Paper
The facility has adopted the Acuity-adjusted staffing model in its respiratory care department. This was conceptualized due to the low number of Respiratory Therapists (RTs) in the facility, therefore, necessitating structural assignment of the RTs to serve all the patients. In the model, a robust Electronic Health Record (EHR) is used to calculate and predict the patients’ acuity scores thereby generating reliable and valid data to inform staffing decisions. A different number of nurses are assigned to the emergency respiratory wing, adult respiratory wing, pediatric respiratory wing and geriatric respiratory wing. Moreover, the model utilizes the client satisfaction metric to evaluate the output of the RTs. As such patient outcomes, mainly inscribed in the recovery rate is used to determine the feedback of care delivery.
Effectiveness of the Model
Firstly, the model ensures the right RTs are assigned to the relative patient based on the initial assessment of the patient’s needs. As such, the preservation of human resource is ensured eliminating occurrences of waste such as a nurse with irrelevant qualifications being assigned to a patient that may result in exhaustion and loss of morale (Al-Dweik & Ahmad, 2019). Moreover, patients who least need the RTs can be attended to by fewer professionals thus reducing the stretch on the workforce.
The systemic assignment of professionals has also resulted in optimized patient outcomes. By creating a balance of workloads, the nurses can attend to needier patients first thereby preventing avoidable sentinel events before they prevail. The acuity-adjustment model thereby reduces the work pressures associated with nurses having to move from one patient to another by facilitating specialization. The pressures normally reduce the output of nurses thereby negatively impacting patient outcomes. However, by ensuring the RTs deliver in fields they are more proficient in, they better serve the patients enhancing the recovery rates.
Lastly, the model has also been phenomenal in saving the facility from a reasonable financial burden. As Smith et al. (2017) observe, RTs in acute care must be properly remunerated for better service delivery. The model ensures only enough RTs are needed thus reducing the cost incurred in excess staffing. Moreover, the operational costs in respiratory acute care settings are also compounded thus prolonged hospitalization of patients leads to accumulation of healthcare costs. However, the model has ensured the hospital only retains professionals with positive outputs measured through customer satisfaction. With the retention of a productive staff, the patient outcomes are significantly improved thus reducing the admission periods and the hospitalization rates. This significantly improves the healthcare costs, thereby enhancing the effectiveness of the model.
Assignment of Work
Though signifying total disregard to equality, the unit-based assignment of roles is the model exhibit a desirable level of equity. The acuity score is calculated in each unit after every shift based on the intervention required. The above is evidenced by the assignment of an emergency score of one to patients requiring just room air thus assigned fewer nurses and RTs. Patients on ventilators require more attention since their condition is highly compromised and are assigned an emergency value of ten on a Linker scale of one to ten. They are therefore assigned more caregivers.
The scores are posted before every shift change to ensure the assignment of roles in each shift is equitable. Moreover, it is upon the initial patient conditions that the output of the caregivers is denoted. At the end of the shift, the nurses and clinicians get to effectively map the clinical documentation of the prevailing conditions. These outcomes, denoting the productivity are calculated against the level of staffing, thereby ensuring a just mechanism of medical evaluation. Consequently, the system used ensures equity not just in terms of work distribution but also worker’s output evaluation in the facility.
Workflow for Tracking Productivity
The acuity staffing model highly relies on the automation of processes in the facility thereby ensuring a necessitating a fully functional EHR that is used to digitalize all functionalities within the facility. The software ensures that all the workflow processes are synchronized from the condition of the patients in the care units, medication needed to the final condition of the patient after therapy (Zheng, Ratwani & Adler-Milstein, 2020). It is through the bedside digital monitoring system that the vitals of patients are continually assessed and automatically relayed to the EHR system to be accessed by the hospital management. Moreover, the workflow system has four fundamental elements that facilitate its efficiency.
Automated Patient Admission and Discharge system
A contributory factor to the Relative Value Unit used to assess the productivity of healthcare professionals in the facility, the system provides for an accurate way of outlining the physical and pathophysiological conditions of the patients. Moreover, it reduces delays established in the manual handling of discharge and admission processes. The nurse can therefore access the recommendation of the physician as well as issuing their own based on the evaluation of the medical condition of the patient (Berwick & Gaines, 2018).
The self-service portals are created for the physicians, nurses and RTs as a user-friendly mechanism of making requests, tracking the shifting schedules and seeing the patient conditions in their assigned units. Through this, the operations are eased, as well as allowing for the evaluation of employee performance through regular posting of their job results.
The patient survey system
The respiratory section is mandated to the provision of holistic care rather than just performing medical therapies. By providing an electronic survey system that records the satisfaction level of patients within the healthcare units, it is easier to evaluate staff performance. The system is one way with no option for probing the reason behind patient satisfaction levels to eliminate the possibility of staff interfering in the process.
This forms the workflow engine that involves the continuous update of patient records comprehensively by each healthcare professional who handles the patient. According to Nascimben (2020), automation ensures the consistent update of records providing a valid response point for making therapy-related decisions. Additionally, the system can automatically make decisions based on a pre-programmed set of rules. The decisions form guidelines for the RTs and other caregivers’ actions in the care units.
Room for Improvement
Elimination of false alarms is the first avenue that provides for the enhancement of safety and productivity. The bedside vital monitoring system can be integrated to allow for only genuine alerts to be recorded. This reduces avoidable disturbance of caregivers that may negatively affect their morale. The automated system can also be double-checked by humans as a means of affirming the accuracy of the records contained. Moreover, though the computation of acuity scores may be done digitally, human input in the actual assignment of caregivers in the units offers a great chance of ensuring flexibility of staffing.
The healthcare system is a vast arena with a plethora of operations from appointment and scheduling of patients to the evaluation of healthcare workers. Given the scope of activities, it is a challenge finding a workflow system that efficiently caters to all the processes. Moreover, regulations regarding healthcare such as HIPAA that regulates the collection of personal data also restrict the wholesome use of one workflow system. This suggests that they should be used in a complementary way to achieve the objective of easing healthcare operations to improve patient outcomes.
- Al-Dweik, G., & Ahmad, M. (2019). Matching Nursing Assignment to Patients’ Acuity Level: The Road to Nurses’ Satisfaction. Journal Of Nursing Measurement, 27(1), E34-E47. https://doi.org/10.1891/1061-3749.27.1.e34
- Berwick, D. M., & Gaines, M. E. (2018). How HIPAA harms care, and how to stop it. JAMA, 320(3), 229-230. https://doi.org/10.1001/jama.2018.8829
- Nascimben, D. (2020). Flexible pathway orchestration engine for healthcare using BPMN and workflow systems. OEV Publication. http://urn.fi/URN:NBN:fi:aalto-202008234988
- Smith, S. G., Endee, L. M., Scott, L. A. B., & Linden, P. L. (2017). The future of respiratory care: results of a New York state survey of respiratory therapists. Respiratory Care, 62(3), 279-287. https://doi.org/10.4187/respcare.04768
- Zheng, K., Ratwani, R. M., & Adler-Milstein, J. (2020). Studying workflow and workarounds in electronic health record–supported work to improve health system performance. Annals of Internal Medicine, 172(11_Supplement), S116-S122. https://doi.org/10.7326/m19-0871