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A recent industry report describes artificial intelligence (AI) as ‘a self-running engine for growth in healthcare with immense power to unleash improvements in cost, quality and access. Growth in the AI health market is expected to reach $6.6 billion by 2021— a compound annual growth rate of 40%. In just the next five years, the AI health market will grow more than 10X2.’

This is an encouraging number, especially after the unprecedented disruption the coronavirus crisis has brought about, with the healthcare industry being at the center of the pandemic—from frontline healthcare workers nursing the affected, to the untiring research towards developing a vaccine. This (healthcare) is a sector which relies as heavily on state-of-the-art medical devices as it does on skilled personnel.

AI in this sector represents ‘a collection of several technologies that help machines sense, comprehend, learn and perform administrative and clinical functions.' Legacy technologies comprise algorithms/ tools that complement humans; in sharp contrast, cognitive AI today is augmenting human activity by performing (cognitive) tasks like seeing, writing, moving, reading and analyzing data.

To quote Frost & Sullivan, ‘clinical support from AI will strengthen medical imaging diagnosis processes. In addition, the use of AI solutions for hospital workflows will enhance care delivery. Overall, AI has the potential to improve outcomes by 30% to 40% while cutting treatment costs by as much as 50%.’

Actionable clinical intelligence through AI-driven predictive analytics

The analytics industry’s evolution is leading to AI being increasingly leveraged to create accurate predictive models. As AI’s rate of adoption rises in the healthcare industry, it is unlocking the true potential of predictive analytics – and clinical decision support – for organizations in this space.

Predictive analytics (across industries) use historical and existing data to identify behavior patterns, which help foretell – almost accurately – future events. In healthcare, this is especially critical because being a step ahead in emergency and intensive care (or surgical) events can facilitate quick and informed decisions which can save patients’ lives. In other words, predictive analytics alerts clinicians and caregivers beforehand of events occurring and their likely outcomes, helping prevent and cure health issues.

Driven by AI, algorithms today are being populated with historical and real-time data to predict nearly accurate outcomes. These predictive algorithms can support clinical decision making for individual patients, as well as interventions for entire populations. They can be used to overcome hospitals’ operational and administrative hurdles too.

Suffice to say that be it providing accountable and cost-effective caregiving or population health management, predictive analytics is as much about reducing risk as it is about anticipating it.

Use cases for predictive analytics in healthcare

While predictive analytics is extremely useful when it comes to taking immediate action, there is also a case to be made for events which require considered thinking. For instance, provider and payer organizations are using predictive analytical tools to overcome data security, financial and administrative challenges and are gaining significantly in the efficiency and consumer satisfaction departments.

In this blog, I have listed the keyways healthcare organizations can extract meaningful and actionable insights from their constantly growing data assets by deploying predictive capabilities.

1. Detecting early signs of health status deterioration through risk scoring –

Prevention is better than cure; prediction helps in prevention through risk scoring – for chronic diseases as well as an entire population’s health. Predictive algorithms help in the early identification of patients who need intervention urgently, as well as those with higher risks of contracting chronic diseases.

Such algorithms are now being deployed in tele-ICU settings which monitor patients remotely, significantly reducing the need for the constant presence of bedside clinical caregivers.

2. Delivering predictive care for at-risk patients in their homes –

Predictive analytics is equally effective in delivering value in a virtual setting and within individual residential environs – by preventing patients from backsliding into a need for acute care. It does so by identifying patients at the risk of emergency transport by combining – and analyzing – data from hospital electronic medical records and medical alert services among other sources. By enabling healthcare organizations to proactively reach out to such patients, predictive analytics prevent unnecessary hospital readmissions and associated costs.

3. Identifying equipment maintenance needs before they arise –

Predictive analytics can identify component replacement, or maintenance needs, of medical equipment by analyzing data relayed through sensors. For instance, the parts of an MRI scanner degrade through regular use over time and predictive analytics can help schedule maintenance at a time when it is not in use. Running this kind of prognostic helps minimize unscheduled interruptions in workflow.

4. Enhancing the efficacy of the supply chain –

The supply chain is among the largest cost centers for any healthcare organization in cases of unexpected disruptions and associated spends. Predictive tools can reduce variation and provide more actionable insights into ordering and use patterns, helping trim unnecessary pay outs and boost efficiency.

5. Ensuring data security and confidentiality –

Cyberattacks are increasingly becoming sophisticated – a phenomenon that needs countering by putting stringent cybersecurity measures in place. Predictive analytical tools monitor clinical data access, sharing and utilization patterns to pre-emptively warn healthcare organizations when something changes – especially when such changes indicate possible infiltration by an intruder. For instance, a ransomware attack.

At Columbus, our experts leverage AI and Machine Learning techniques to predict outcomes and trends, followed by prescribing actions that uncover new growth potential and increase business efficiency that is to your competitive advantage. We specialize in digitally transforming companies by deploying data and analytics-oriented solutions to help create winning organizations. Write to us at us-marketing@columbusglobal.com to learn more.

[Webinar] Predictive models with Azure machine learning


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Pricewaterhouse Coopers forecasts that Artificial Intelligence (AI) could contribute up to $15.7 trillion to the global economy by 2030, of which $6.6 trillion is likely to come from increased productivity and $9.1 trillion from consumption-side effects.
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