Healthcare as a Service (HaaS) Through Data-driven Decision Support Systems
Take a look at the health system today, how has the practice of medicine improved as opposed to what was the ground norm some decades, centuries, and possibly millennia ago? One of the cornerstones of research in clinical practice is the improved outcome in managing disease conditions and treatment procedures.
Take the prognosis and treatment of Cervical cancer as a case study. According to Cancer Research UK, over 3,300 women are diagnosed with cervical cancer in the UK annually. The numbers have it that there are around 860 yearly UK deaths from the disease between 2018, 2019, and 2021. In a six-week induction chemotherapy (the initial chemotherapy a person receives before undergoing additional cancer treatment) procedure which predates the chemoradiation, it was observed that more people survived without their cancer returning. After 5 years, 80% of trial participants who received induction chemotherapy followed by CRT were alive and 73% had not seen their cancer return or spread.
This breakthrough research, which was a product of working through data-driven trial stages of treatment, delivered a desirable protocol for treating this type of cancer. The result provides a template for the clinical development of other treatment procedures and will create interest in the modification of other cancer research procedures.
Overview of Healthcare Inefficiency
The inefficiency of healthcare reflects the underinvestment in healthcare as a service (HaaS) to the growing global populace. Events show that the treatment of malaria (in Africa) was built without data for far too long and this affected public trust in the process. Not only did public trust wane but the course of malaria research and the management of disease processes were also affected. Again, this development spiraled down to the development of Malaria vaccines, which was not only problematic but spasmodic in the pronounced success
However, the management and treatment have been stellar elsewhere except for Africa, where these processes were hampered by logistical inefficiencies occasioned by poor data-driven planning processes. Firstly, the decisions made by governments or organizations that allocate resources for Malaria control programs relied on insufficient and poor supply chain procedures to distribute funds meant for this research process. The consequence was the shortage of essential medications, and preventive procedures, and a scarcity of supplies such as nets, insecticides, and other resources for treatment.
Another failure was the inability to efficiently capture and utilize epidemiological data to inform decision-making processes. This led to the misallocation of resources. Politics and political choices also interfered with the normal distribution of vaccines. The distribution of vaccines is allocated per prevalence basis — which is from the regions of higher occurrences to places where the occurrence is lower. Religion didn’t help matters as it also prolonged the failure of proper data capture.
In Muslim countries, there was heightened suspicion about the Polio vaccines. The belief was that the polio vaccine had substances that could potentially cause infertility among girls and was a plot to depopulate Africa. There were further claims that the polio vaccine contained impurities that could infect those immunized with HIV and cancer. Conspiracy theories, beyond what is captured in this article destroyed the orientation and processes that had worked elsewhere.
All these were happening against contravening evidence that From 1988–2006, more than 5 million children were saved from paralysis using more than 10 billion doses of the oral polio vaccine (OPV) to vaccinate more than 1 billion children and that no serious side effect got recorded within this period. The result of this is that the disease is endemic in Muslim countries, especially in places like India, where the states with vast Muslim populations are mostly affected.
Introduction to data-driven decision support systems (DDS)
Data-driven decision support systems (DSS) are software tools or protocols that assist decision-makers in making informed choices. They do this by analyzing large volumes of data to arrive at a logical decision. These systems can integrate data from various sources, such as databases, spreadsheets, and external sources like the Internet. The application of these support systems provides advanced analytical insights and techniques that can be helpful in the management of disease processes and also in planning the course of treatment, with an expected outcome in mind.
With a data-driven support system, several key features work to optimize and most importantly, streamline the often long-routed decision-making processes. Firstly, it facilitates data integration through the collection of valuable information from multiple sources. It then consolidates this information into a standardized format for analysis.
By adopting various statistical and analytical techniques, these systems uncover patterns, trends, correlations, and anomalies within the data feed. Present within the data feed is the visualization through graphs and charts — these aid decision-makers in understanding complex information. With the process optimized, the prediction and forecasting of future events come easily through the leverage provided by historical data and predictive modeling techniques.
With these decision models incorporated into any adopted system, users can evaluate different scenarios and make optimal choices based on the analysis available. Some of these advanced systems offer real-time updates which ensure timely decision-making with up-to-date information.
Purpose and significance of implementing DDS in healthcare
Several scenarios play out in the deployment of DDS in healthcare, but in all, The DDS data-centric, network-based information and data-management system is pivotal to the scalability and advancement of healthcare processes like; telemedicine, patient healthcare management records, radiodiagnistics, cancer treatment planning and the quality of emergency services. Therefore targeted efforts at improving its reliability, robustness, and distributed operation must be measured and periodically assessed to ensure alignment with trends.
A typical multipronged approach to this is how Aimed Analytics organizes data in the ever-evolving landscape of healthcare technology. Aimed Analytics pioneers and dedicates its efforts to unlocking the full potential of medical data. With a focus on revolutionizing the industry through innovative data analysis solutions, and empowering healthcare professionals with the insights needed to make informed decisions, it promotes an AI-powered modular system, which overlays a significant leap forward in fully exploiting medical data.
Through the labyrinth of data, the company offers a comprehensive suite of tools tailored to the intricacies of medical data. In other words, Aimed Analytics ensures that healthcare organizations can navigate the data landscape with confidence. As the healthcare industry embraces the power of data-driven decision-making, we can refer to the Aimed Analytics approach as a form of innovation, poised to transform patient care and treatment outcomes.
Leveraging data to provide a robust information channel in healthcare management is the go-to for technological advancement. It is indubitable that this clinical decision support will effectively improve patient outcomes and lead to higher-quality health care. So, companies, healthcare practitioners, academics, and researchers have the providence of DDS, which encapsulates the adoption and rapprochement of healthcare as a service.
With the provision of timely information, usually from the point of care, informed decisions about a patient’s treatment protocols are easily arrived at. We live in an era where information is shared in a fraction of a second, so these tools will not only bring a revolution to healthcare practice and patient management but they will provide a huge relief for practitioners and a new outlay for knowledge advancement and curricular development.
