AI is the future of UK Healthcare

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It’s not whether AI will transform healthcare, but when.

Applying AI in healthcare is already changing the patient experience, how clinicians practice medicine, payers improving expediency to patients, care providers improving outcomes, life sciences and the pharmaceutical industry improve operating efficiencies . The journey has just begun.

The applications could involve diagnosis and treatment recommendations, patient engagement and adherence, readmission, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans.

0of respondents believe AI will improve the patient experience.

AI in Healthcare

AI is changing healthcare, reshaping the business models’ providers in the value chain as access to data is increasingly democratised. Predictive Analytics and Machine Learning is also now being democratised by providers to solve critical key health delivery challenges and explore opportunities thus transforming the value chain.

Our automated machine learning platform makes advanced predictive analytics more accessible by reducing barriers to more accurate predictions. Download this e-book to learn about specific use cases, overcoming barriers to entry and ultimately how to deliver better health outcomes and better patient experiences with automated machine learning.

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Challenge 1

Solution 1

Insurers

Automate and inform traditional processes, with new technological capabilities that empower users to optimise every function in the insurance value chain.

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Be a leader: in CAHPS, HEDIS, and Medicare Star quality ratings with superior healthcare analytics
Determine which members: are at risk of leaving your health plan
Improve risk adjustment: and capture the best target opportunities
Flag potential: fraudulent claims
Build precise financial: actuarial, and underwriting models for cost of care, IBNR, MLR, large claims forecasting, and premium pricing models
Use analytics: to understand member hospital inpatient length of stay and risk for readmission

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Challenge 2

Solution 2

Providers

Healthcare organisations must embrace new technology that helps improve outcomes, streamline operations, identify financial risks, and create healthy customers.

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Build more precise: patient readmission risk models
Optimize your revenue: cycle management and revenue prediction
Accurately forecast staffing needs: with predictive analytics in healthcare
Actively manage your patient: population health and accurately stratify your patient population risk
Use analytics to understand: patient length of stay and patients at risk for hospital-acquired conditions
Be a leader: in value-based healthcare

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Challenge 3

Solution 3

Suppliers

To predict the range and price of stock and inventory required to provide shipping to customers who want to purchase certain products and have them delivered on their terms.

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Leverage predictive analytics: to optimize your patient marketing campaigns, messaging, and call center operations
Increase renewals and reduce customer turnover: while actively managing the efficiency of your sales force
Forecast product sales: more accurately
Build more effective: patient/customer messaging
Optimize: your patient/customer messaging
Integrate AI-driven insights: into clinical solutions, patient engagement applications, and healthcare operating systems
Be a leader: in supply and demand chain planning with exceptional analytics

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All use cases underpin increasing revenue, reducing costs and delivering excellent customer service

High value AI use cases for Healthcare

Reduce Readmissions

Patient readmission results in significant costs for payers, hospitals, and patients. Using our enterprise AI platform to predict and prevent hospital readmissions leads to more efficient use of scarce hospital resources while improving the overall quality of care that patients receive.

Accurate Predictions About ICU and ED Utilization

High costs and periodic scarcity of critical care resources are two key reasons why ICU operations must be improved. Using an enterprise AI platform to create models that more accurately predict which patients need and do not need intensive care and to anticipate optimum staffing levels represents an enormous cost saving opportunity for hospitals.

Determine which patients or members are less likely to adhere to prescribed drug regimens

Patients or members with chronic diseases who do not consistently take their medications lead to more than $100 billion in preventable costs annually. Using Automated Machine Learning to create models that identify the patients or members who are less likely to adhere to prescribed drug regimens and predict their behavioral drivers helps create the right intervention plan to increase medication adherence.

Identify Fraudulent Payment Activity

Healthcare fraud can be costly, but it is also too expensive and inefficient to investigate every suspicious claim. Using our enterprise AI platform, organizations build accurate predictive models to identify and prioritize the most likely fraudulent activity, allowing for more effective deployment of resources to investigate, and optimization of customer satisfaction.

Flag members or patients who are at risk for churn

Payers and providers lose money when members decline renewal with their health plan or when patients do not return to their facilities. Payers and providers can use Automated Machine Learning to incorporate patient retention risks into their organization’s workflow, leading to reductions in non-renewals and patients not returning for further treatments.

Alert providers to patients at risk for hospital-acquired conditions

Patients are more susceptible to bloodstream infections during hospital stays. This is a costly outcome that often leads to hospital readmissions. Our platform can predict which patients are more likely to contract sepsis or CLABSI and automatically alert doctors to run additional diagnostics and testing.

Predict patient or member length of stay

One of the primary predictors of cost is length of stay. Longer stays not only place patients at higher risk of hospital-acquired conditions, but they also constrain hospital bed availability and physician time. Automated Machine Learning can help providers and payers predict the length of an inpatient stay, which creates greater scheduling flexibility, reduces costs, enables targeted interventions for at-risk patients, and helps organizations transform to value-based care.

Predict No-Shows

No-show patients are costly for providers and payers. can also be costly for patients because skipping medical appointments can lead to untreated medical conditions and adverse health outcomes. DataRobot can help predict which patients are most likely to be no-shows, equipping decision-makers with the information they need to rearrange schedules, craft interventions, and proactively engage patients. Automated Machine Learning can also aid in determining the probability of non-adherence and whether patients will respond to outreach efforts.

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Chief Data Officers

Increase the productivity of your data science team.
With automated machine learning, you can get the productivity of a large data science team from a small one. Let DataRobot find the best models for you and use DataRobot’s simple deployment options to get them to market faster. Relieve data scientists from documentary requirements by using DataRobot’s automated model risk management and model validation templates.

Chief Technology Officers

Bring AI and machine learning-based solutions to market faster.
Get models to production faster using DataRobot’s low-risk model deployment options, including code generation, deployment to Spark, and API-based deployment capability.

Chief Data Scientists

Annihilate your backlog of analytics requests.
Let DataRobot suggest the best model in each situation, saving you the time and effort of trying and comparing every model. Use automated machine learning to build many models at the same time it took to build one, increasing precision with more model granularity. Let DataRobot handle the low-risk models from start to finish so you can focus your talent where the payoff (or the risk) is the greatest.

Chief Information Officers

Monetize your investments in data infrastructure.
The bottleneck in many banks is no longer a lack of data, it’s plenty of data but not enough analytics staff to turn that data into insight. Democratize data science with DataRobot and watch the performance of your business take off as the data reveals opportunities and improvements.

Chief Marketing Officers

Hyper-personalise your offers direct to consumers.
Research has shown that brands adopting the technology and strategies to enable personalised digital experience will gain competitive advantage. They find it easier to acquire customers, enable faster conversions and create lasting engagement and loyalty.

AI Success Stories

01 / 03
“We are using the AI Platform to make some pretty huge decisions at Steward Health Care. It’s very much a part of our growth strategy. It’s uncharted territory for healthcare”
– Executive Director
02 / 03
“Explainability is key in healthcare, and the AI platform makes it easy for our clinicians to understand the predictions and take action on those insights”
– CIO
03 / 03
“The AI platform as a competitive advantage in healthcare. How else are you going to improve quality rates, or drive down medical costs, or reduce member churn, if not AI? It’s an obvious choice”
–Chief Analytics Officer