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Healthcare AI

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AI Transformation in Healthcare

The healthcare industry is evolving rapidly with large volumes of data and increasing challenges in cost and patient outcomes. Early adopters of AI in the healthcare space are reaping the benefits in terms of patient care and adding to their bottom line results, and everyone is taking notice.

These companies are using AI for a number of scenarios including managing claims, detecting fraud, improving clinical workflows, and predicting hospital acquired infections. WooHoo Ai, the open source and automation leader in AI, is empowering leading healthcare companies to deliver AI solutions that are changing the industry.

Predicting ICU Transfers

Saving Lives by Catching Patients Before the Crash with AI


Studies show that patients who undergo an unplanned transfer to the ICU experience worse outcomes than patients admitted directly. These patients typically stay in the hospital 8 to 12 days longer and have significantly higher mortality rates – these patients account for only 5% of patients but represent one-fifth of all hospital deaths.

The challenge is to find patients before they “crash” and need to be moved to the ICU, but these patients often don’t have symptoms that clinicians can recognize as leading to a serious change in condition.


AI models can be used to find patients who are likely to crash. The machine learning models use patient medical records, laboratory results, and vital signs from patients to find early warning signals of deteriorating condition.

These models can then be used with existing patients in realtime to determine their risk of a crash and as part of an early warning system for clinicians so they can intervene before the ICU transfer is needed. The AI system can also provide reason codes for a particular patient, which can help clinicians understand where they should begin their treatment.

Medical Testing

Predicting Medical Test Results with AI


According to the National Academy of Medicine, waste in healthcare is widespread and is estimated to be about a fourth of all the money spent on healthcare each year, a staggering $765 billion annually. One of the key areas of waste is unneeded testing or routine tests that are rarely used. The number of tests can add up for an individual patient and result in significant costs that do not contribute to the quality of care or positive patient outcomes.


AI based solutions can be used to help clinicians make better decisions by narrowing the types of tests that are likely to be useful for a patient. AI models can be created using volumes of patient information from healthcare systems together with data from pharmaceutical companies to predict likely test results a given patients. This model is then deployed into an AI-driven application that can provide indications of which tests are likely to produce definitive or valuable results based on the patient’s medical history and current symptoms. With this knowledge, the clinician can pursue treatments with the best outcomes and minimize the number of tests, which saves time and reduces costs to the patient.

Improving Clinical Workflow

AI Provides a Helping Hand to Clinicians


Clinicians are often overworked, and hospitals are understaffed. Various studies estimate a diagnosis error rate of 10 – 15% which has a huge impact on those patients and the providers. Early diagnosis of critical conditions like Sepsis or Intracranial Hemorrhage has a significant effect on patient outcomes.


AI based decision support and diagnosis can help clinicians make better decisions by incorporating more data into the decision-making process and by learning patterns that are outside the clinicians’ purview. With mobile devices integrated into the clinical workflow, AI-based decision support helps doctors and nurses by providing a second opinion or by pointing out information they may have missed. These additional insights help the clinician make a more informed decision and can actual save time, expense and patient discomfort by preventing unnecessary tests.

Predicting Hospital Acquired Infections (HAIs)

Saving Lives with AI


Hospital or Healthcare acquired infections (HAIs), such as central-line associated bloodstream infections (CLABSIs) are a huge problem for patients and providers. An estimated 250,000 CLABSIs occur in the U.S. annually, according to the Centers for Disease Control and Prevention (CDC). Patient mortality rates associated with CLABSI are up to 25 percent and the cost of the infection is up to $36,000 per episode, according to the CDC.


Using AI driven models, providers can predict which patients are most likely to develop central-line infections by looking and a variety of data including patient information, treatment history and staff history. With this prediction, clinicians can monitor high-risk patients and intervene to reduce risk. AI driven models can also identify the reasons for increased risk and provide reason codes that point clinicians to recommended treatments and preventative measures for future patients.

Claim Denials Management

Streamlining the Claims Process with AI



On average, 24% of claims are denied during the evaluation and payment process, according to the Doctor-Patients Rights Project. Denied claims are large expense for providers and painful for patients who have to pay out-of-pocket or providers who have to write off as losses. Existing claims management processes are highly manual with analysts and rules making choices about which claims to work for resubmission with their limited time.


An AI approach uses machine learning models to streamline the denials management process by finding claims that have a high likelihood of being paid and the highest potential value. By working these claims first, the providers and payers spend the time on those claims that are most likely to be valid and will yield the most value to patients and providers. AI models can also provide reason codes for the denial which streamlines the review process by allowing the investigator to focus on the key issues. Reason codes are also helpful for patients and providers because they can inform them of issues with their claim and help them to fix the claim for reprocessing or change future claims to avoid issues.