After the Covid-19 virus broke out in the world, hospitals and healthcare systems were struggling to predict patient volume, availability of hospital beds, and usage of ventilators for several months. With the rising number of Covid-19 patients, the task of proactively treating such a huge patient census is a daunting task even now.

There have been many epidemiological studies that have forecasted a peak in health service utilization with the rise in the number of patients with Covid-19 symptoms, on global and national levels. However, on a granular level of clinics and hospitals, the significance of predictive analytics has also been widely felt. The Covid-19 virus is still at large, and the second wave of Covid-19 cases is projected to hit right after the economies begin to reopen. In such a scenario, healthcare systems will have to consider a predictive analytics model to plan the usage of their resources if they haven’t already.

Predictive Analytics in Hospital Management

Predictive Analytics uses modeling, data mining, statistical algorithms, and artificial intelligence to evaluate historical patient and infrastructure records. It can also process real-time data from multiple sources. The forecasted insights that come out of this process are actionable opportunities for a hospital, to proactively handle future demands and developments in patient care.

To be able to successfully forecast key metrics in health service utilization through predictive analytics, Electronic Medical Records (EMR) or Electronic Health Records (EHR) data from a hospital is first into an ingestible format for processing. Based on the hospital data, a predictive analytics tool identifies patterns and projects the numbers that would be relevant to the capacity of that hospital.

Predicting future trends in health service utilization

With predictive analytics in healthcare, individual hospitals will be able to forecast patient volume, availability of hospital beds, ICU occupancy, and usage of ventilators with more accuracy. This projection can then be useful to assess the available resources and procurement of infrastructure for the hospital in advance, to efficiently treat patients having Covid-19 or any other ailments.

Even beyond the Covid-19 pandemic, predictive analytics in healthcare systems can prevent bottle-neck situations in critical care. For example, real-time data with a complex-event processing algorithm can ensure optimal usage of Operating Rooms (OR) and hand-overs. It will avoid delays in OR availability and raise the chances of saving more lives.

In light of this pandemic, the advantages of predictive analytics in healthcare are undeniable. Predictive Analytics technology is arguably the future of medical or clinical informatics but when we talk about real-world applications, certain risks and concerns are also part of the package.

What are the risks and concerns?

  • Complexity of Healthcare Decisions

To begin with, healthcare is ultimately about caregiving and helping people. When caregivers make judgments to treat patients, the approach is guided by ethical practices and human thinking. We have definitely advanced quite a lot in terms of technology but human thinking and decision making processes are still very different from a machine’s. Implementation of predictive analytics in healthcare systems is yet to cover a seamless yet ethical communication between machines and healthcare professionals.

Complete reliance on automated systems powered by predictive analytics could potentially elevate risks. Acceptance of the outcomes of predictive analytics should be discretionary in nature, not absolute. Healthcare professionals or doctors should be able to intervene in the process. They should be able to change suggestions, recommendations, and prescriptions based on their judgment, whenever deemed appropriate.

  • Ethical Conundrum

As much as we would like to believe that through data, statistical modelling and predictions, healthcare management will be made easy, we usually don’t think about the medical and moral ethics that healthcare professionals pledge to abide by. Data of a patient, his ailment, and his medical records have to be kept private and protected to uphold the standards of the hospital’s ethics. When we talk about using historical data to feed predictive analytics, we do not consider that patients are stakeholders in this scenario as well. Patients’ confidentiality rights are a key concern when it comes to entrusting machines and algorithms with the preset assumptions of choice architecture.

  • Algorithmic Bias & Loose Regulations

Technology is known to be unbiased, but that is not completely accurate. Although technology, in itself, is unbiased, it is still programmed by humans who have prejudices and biases. When humans use their judgment to design a choice architecture in statistical modeling, consciously or unconsciously, they do make the algorithms biased. It becomes more hazardous when improvements in algorithms are not being made regularly. In the absence of a continuous feedback loop, the bias in the system can’t be reduced regularly, making it extremely error-prone. In the aspect of healthcare, errors can potentially risk lives.

On top of it, Government regulations all over the globe do not clearly define ethical methods of algorithm development, leaving it on the developers to voluntarily consider risk models, reduce bias and privacy issues in their systems.

Predictive analytics is definitely the future of healthcare. Nevertheless, we have to understand that predictive modelling and automated systems are not flawless. We, at Enquete Group, believe that Predictive analytics in healthcare should be a transparent, ethical and accountable process with human intervention at necessary junctures of decision-making. Reach out to Enquete Group’s team of Data Scientists to explore the application of Predictive Analytics in Hospital Management, the right way!

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