loader
AI in Healthcare

AI Strategies in Healthcare

. Health organizations are beginning to use AI Strategies In Healthcare for a number of scenarios including claims management, fraud detection, clinical workflow improvement, and hospital-acquired infection prediction. The healthcare industry is evolving rapidly with large volumes of data and growing challenges in terms of patient costs and outcomes

AI in Healthcare

1.1. Leading Health and Human Services Innovation AI

AI Strategies

Hekate will prioritize the application and development of AI strategies in healthcare in the areas of the enterprise’s shared mission in health and human service innovation. Hekate will continue to lead the way in identifying opportunities for mission-oriented, risk-reducing AI solutions appropriately, based on a common framework of guidance under applicable law.

Hekate’s key missions include: Regulating and monitoring the use of AI in strategies the healthcare industry Hekate’s liability covers all aspects of healthcare including standards for service delivery healthcare, payments, medical device software, food and medical products, and privacy to ensure compliance, safety, and efficiency. AI can be leveraged to reduce regulatory burdens and streamline processes that drive advances in human health and well-being. To harness these benefits, Hekate will continue to develop standards that inform safe and transparent AI policy and guidelines, and encourage agile and adaptive innovation.

Specifically, these efforts include, but are not limited to, advancing biomedicine through AI-powered insights into big data sets, predictive analytics in surveillance, and public health response, while promoting the use of cognitive technology to identify novel approaches to health conditions and complex multifactorial causation behaviors. Hekate will also implement AI within the licensing process itself, for example, to facilitate risk-based funding review, to optimize resource allocation and reduce (detection, prevent, reduce) opportunities for waste, fraud and abuse of public money.

1.2 Partnering and Responding to AI-Driven Approaches within the Health Ecosystem

Hekate will prioritize the application and development of AI across business mission areas to enable organizations to enable organizations to meet the dynamic, shared needs of different partners for health and human service innovation, including: Collaboration with internal partners, external partners, including academia, the private sector and governments to enhance programs and services through the potential of AI. Hekate collaborates between partners in private industry, academia, and various governments to advance common interests

across shared missions in the management of human services and essential health programs. Hekate continuously strives to identify and pursue opportunities to use population and individual health data to improve the public health of people. This vast knowledge and data pave the way for AI-driven solutions to better understand trends, outcomes, and opportunities in the health and human services ecosystem. Hekate, at the enterprise level, will position itself to facilitate public-private partnerships (PPPs), including those that can be done at the Hekate level.

It is anticipated that these partnerships will be among a number of approaches that can link AI technology with the support necessary to succeed: (1) government and outside expertise, engage in mutual dialogue, (2) appropriately shared external and government datasets, models, and algorithms, linked together, and leveraged for insight, (3) policy support and risk-based monitoring tied to trusted AI, and (4) well-defined use cases tied to the advancement of health and wellbeing, with criteria clear on success, interoperability and reusability.

ai in healthcare

Today AI is expanding the opportunity and decreasing risk in healthcare today. Payers and providers are increasingly using AI in a range of applications including claims management, fraud detection, disease risk assessment, and to improve clinical workflow. The list of healthcare use cases continues to grow with major payer and provider groups working on an AI strategy

AI in Healthcare

Before: Sepsis diagnosis and treatment only happen after severe and damaging symptoms.

With AI: AI models predict Sepsis diagnosis hours before onset using routine data.

Sepsis is the leading cause of preventable death in hospitals with sepsis mortality increasing by 8% per hour if treatment is delayed. Up to 80% of sepsis deaths could be prevented with prompt diagnosis and treatment. Diagnosis of sepsis can be difficult because its signs and symptoms can be caused by other disorders and there are no reliable biomarkers before onset. Doctors often have to order a series of tests to try to pinpoint the underlying infection, which will further delay treatment. AI-based diagnosis can help clinicians identify patients at risk for Sepsis using routine vital signs and patient history. This diagnostic aid helps clinicians order more relevant tests and initiate treatment earlier, which promotes better patient outcomes but also reduces costs for providers and payers.

Before: HAIs diagnosed only after the fact with a 40% mortality rate.

With AI: AI models helps to identify high risk patients so that clinicians can monitor and treat them.

Hospital or Healthcare acquired infections (HAIs), such as central-line associated bloodstream infections (CLABSIs) are a huge problem for patients and providers. Up to 25% of CLABSI patients die from what is principally a preventable problem. 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 patent information and treatment history. With this prediction, clinicians can monitor high-risk patients and intervene to reduce risk.

Before: Clinicians making decisions without the full picture or advanced insights.

With AI: AI models integrated into applications provide diagnosis and decision support for clinicians.

Clinicians are often overworked and understaffed. Various studies estimate a diagnosis error rate of 10 – 15% which has a huge impact on those patients and the providers. 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 actually save time, expense and patient discomfort by preventing unnecessary tests.

Before: Up to 25% of patents are readmitted to the hospital within 30 days after treatment.

With AI: AI models predict which patients are at highest risk, so care can be managed.

Patients with serious and chronic illnesses are treated in the hospital and then discharged. Unfortunately, up to 25% of these patients will be readmitted within 30 days to be treated again. With a focus on value based care, providers are trying to prevent unnecessary readmissions. This process works by finding high-risk patients for readmission while they are hospitalized and then targeting them with different treatments in the hospital, defining different actions during discharge, and taking steps post discharge to ensure compliance with home care regimens. The benefit of this improved process is a more successful outcome for the patient and lower costs for patients, providers and payers.

Before: Rules catch some fraudulent claims. Others are paid and then investigated later.

With AI: AI models identify claims that are likely fraudulent and flags them for review.

Tens of billions of dollars in fraudulent healthcare claims are likely filed each year which contribute to the increased cost of care and higher healthcare premiums for patents. Rules based fraud detection is easily fooled with new techniques emerging daily and manual review cannot scale across billions of claims. An AI approach to fraud detection scans each claim to look for patterns that indicate fraud. A real-time machine learning approach can keep track of existing patterns and look for those while also learning from new patterns as they emerge. AI based fraud detection helps find fraudulent claims in the system in real-time before they are paid, which reduces costs for payers, helps keep costs lower for patients and helps catch fraudsters in the act.

Hekate must continue its leadership at the vanguard of health and human services innovation to meet the dynamic needs of people. Trustworthy, ethical, and intentional use of AI technologies will accelerate Hekate’ ability to meet these evolving needs as both a leader and responsive partner in innovations across health and human services. This strategy lays the foundation upon which Hekate can use to drive change across the organizations by encouraging the application of AI to promote advances in the sciences, public health, and social services—improving the quality of life for all.