The Use of a Decision Support System (DSS) in Patient Care

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Background
Home Health Agency (HHA) is a group of organizations that provide home care health services with a specific focus on nursing care. HHA is currently experiencing logistics management problems in that nurses generally have to traverse long distances to provide timely and high-quality care to patients. Another challenge directly relating to this challenge is ensuring optimum deployment without overburdening the nurses: the agency is presently serving a large geographical region, and nurse burnout is becoming an increasingly prevalent phenomenon. Nurses who travel long distances and work longer shifts are likely to experience sleep deprivation and develop burnout (Dall’Ora et al., 2020). Travel nursing is weary and deprives nurses of the time they need to rest, socialize, or develop their careers. Therefore, developing manageable workloads by reducing the distance and geographical span covered by travel nurses is essential to addressing burnout. On the whole, HHA’s logistics problem is not only affecting nurse engagement but also the quality of care they can give.
Clinical Problem Statement
There is a growing need to improve service provision, the quality of healthcare, and patient satisfaction without compromising the health and wellbeing of nurses by overburdening them with long shifts and journeys. HHA needs to minimize travel time and distances covered by its nurses, improve its healthcare service quality and patient safety levels, and reduce organizational fuel expenditures in order to establish a competitive edge.
Purpose of the Change Proposal
This capstone project proposes the use of a Decision Support System (DSS) based simulation strategy and optimization model with electronic patient records integrated with geographical information technology. The DSS is a computerized program that facilitates determinations, judgments, and courses of action in an organizational setting: it is used to determine and judge the course of action in a complex network of variables (Govindan et al., 2020). It is an information system that can support HHA in decision-making activities involving analyzing factors or scenarios, prioritization, or involving a sequence of actions. Optimization models are also essential since they help reduce the geographical disadvantages currently faced by HHA, such as overcoming the distance hurdles between a patient’s home location regarding the organization (Zikhathile & Atagana, 2018). On the other hand, geographical information technology will be critical to charting resources and geolocational services that are progressively becoming prevalent in healthcare (Boulos et al., 2019).
A combination of all these technological solutions will allow accurate and efficient visual representation of various locations within the geographical area HHA serves, over and above, to assist in determining the shortest distances between nurses and their patients. An HHA logistics plan that employs electronic patient data, DSS, optimization models, and GIS will help increase healthcare efficiency by optimizing nurse workloads. For instance, the logistics system can identify an overabundance of nurses serving a small geographical area and suggest other areas along with the shortest distances to those regions in order to ensure equal distribution of the nurse workforce and lower burnout scenarios.
PICOT Question
The PICOT question for this capstone project is: In aging patients with short-term in-home care, does patient satisfaction increase where a service provider relies on a logistics plan compared to satisfaction when the service provider does not rely on a logistics plan within 2 months of care? The population, in this case, is aging patients in need of short-term in-home care; the problem is patient satisfaction, while the intervention is the utilization of a logistics plan. On the other hand, the alternative or comparison is that the organization does not use a logistics plan while the outcomes are related to patient satisfaction. The time for the intervention is two months.
Literature Search Strategy
The study conducted a systematic review of relevant literature in various clinical databases. One of the relevant studies identified was that by Dall’Ora et al. CITATION Dal203 \n \t \l 2057 (2020), which entailed a theoretical review of 87 contemporary peer-reviewed articles on burnout in nursing. The study sought to determine the causes and consequences of burnout in nursing and employed a mixed research approach. Dall’Ora et al. CITATION Dal203 \n \t \l 2057 (2020) determined that the biggest causes of nurse burnout included high workload, low control over the job, poor social climate/social support, value incongruence, low decision latitude, and low rewards. Another relevant study was that by Govindan et al. (2020). They sought to investigate nurse burnout in public health systems during the Covid-19 pandemic when most hospitals and healthcare profes...