INTRODUCTION
Artificial intelligence to optimize healthcare processes
All across the medical landscape, artificial intelligence (AI) is being implemented to leverage data-driven decision-making to improve efficiencies at scale. Artificial intelligence applications in healthcare leverage complex algorithms to streamline, automate, and optimize key operational processes. The result is a more seamless care environment that links medical researchers, doctors, and laboratory clinicians to machine learning (ML) capabilities, driving more efficient decision-making.
AI holds the processing power to dramatically improve the way doctors evaluate, diagnose, and treat patients across community health settings.
Healthcare expenditure on artificial intelligence is projected to grow 40% in 2023, from $4.4 billion in 2022 to over $6.2 billion, according to Omdia Forecasts. Over 96% of healthcare organizations surveyed across 2022 reported feeling confident or very confident about AI’s ability to deliver better medical outcomes and business results for providers.
“Healthcare is going to grow faster than most other industries, and according to our forecasts, we expect spending on AI in the healthcare sector to be ranked second only to consumers in 2027,” said Andrew Brosnan, Principal Analyst in Omdia’s AI & Intelligent Automation Practice.
“Healthcare companies historically are conservative when it comes to adopting new technology because of the high stakes of patient care and privacy, security and regulatory concerns, but that’s changed rapidly since the 2020 COVID-19 pandemic. The use of AI during the pandemic and proof-of-concept projects is bolstering confidence in the value AI can deliver in healthcare.”
Healthcare IT is an important quotient of medical success as clinical success, operational efficiency, and practice innovation hinge more and more on the ability to leverage cloud-based software to build the right technology stack. Your organization’s digital health ecosystem needs to deliver robust interoperability to fuel data exchange among stakeholders without compromising privacy or HIPAA compliance.
Artificial intelligence, machine learning, and natural language processing (NLP) are transforming the way medical professionals connect with technology to serve their patients. The future of healthcare service delivery is intrinsically linked to the innovative potential of cutting-edge AI-based technologies coming out of the research and development pipeline. In this article, learn more about the innovative solutions shaping the future of healthcare AI.
PHARMACY
Data-driven decision-making with AI
One of the areas of the healthcare landscape where artificial intelligence has already made a tremendous impact is the pharmaceutical industry.
Data-driven decision-making and AI automation are streamlining drug discovery and clinical trial processes, making it easier for researchers to discover new drugs and get them to market quickly. As data-sharing capabilities improve, many medical researchers believe AI will play a vital role in allowing physicians to offer their patients more highly specialized treatments according to their unique physiology and genetic makeup.
They believe that as community health data sets expand and public health records are analyzed at scale, researchers will be able to respond to emerging community health concerns at the macro level faster while offering more effective micro-level interventions in line with new person-centered care directives. AI enhances the ability of pharmaceutical research teams to gain real-time visibility into the success of medical interventions.
In the near future, as digital health services connect regional medical centers, laboratories, specialist clinics, and other spokes in the healthcare nexus, it will be much easier for manufacturers to expedite production timelines and deliver life-saving medicines when and where they are needed by leveraging advanced medical, social, geographical, insurance and other types of data relevant to the drug discovery process.
The number of applications of AI in healthcare will grow in the future
USE CASES
Healthcare AI use cases that drive innovation
Healthcare spending on artificial intelligence solutions is expected to expand at a compound annual growth rate (CAGR) of 29% to reach a value of $13.8 billion by 2027, when it is expected to be the fastest-growing business sector in the world.
Over the last five years, the adoption of AI in healthcare has increased by over 300%, and more than 83% of healthcare providers consider completing strategic investments to improve AI software capabilities to be a key goal for 2023.
These improvements are in no way limited and encompass every touch point linking physicians and patient consumers across the community health system.
Here are some of the broad use cases defining the growth of healthcare AI across the healthcare, medical, pharmaceutical, and wellness industries:
- Enhance clinical efficiency with automated decision support
One of the most powerful areas where AI is transforming healthcare is in the way doctors are using EMR/EHR systems. AI helps physicians to interpret patterns more accurately to deliver more reliable medical treatments. The software acts as an extension of the human clinician’s expertise. It offers the patient a more detailed understanding of what is going on based on all the available clinical information. - Streamline information sharing between stakeholders
Rules-based robotic process automation (RPA) makes it much easier for clinicians to capture the information they need to stay ahead of compliance demands while providing patients the access they need to stay on top of their care. Automated rules-based information sharing ensures that patients and providers have access to the personal health information they need without compromising HIPAA compliance. - Optimize professional development services
AI is transforming the way healthcare organizations work with information. This includes the information driving their training and professional development procedures. As organizations continue to adopt telehealth solutions and services, there are more and more continuing education opportunities being offered using artificial intelligence, machine learning, and NLP applications. - Deliver on the promise of person-centered care
In recent years, the medical industry has started offering patients access to more personalized services. AI is delivering on those promises and making it much easier for patient-consumers to navigate the healthcare nexus and get the answers, access, and opportunities they need to reduce the overall cost of care while receiving a higher overall quality of care. - Secure business success
Healthcare decision-makers are using robotic process automation, machine learning, and augmented reality applications to model complex processes and achieve real-time optimization of multi-faceted business processes. The result is a much deeper understanding of what is driving success and how to secure partnerships, contract terms, and opportunities to maximize revenues and limit exposure to risks and liabilities.
REGULATION
Legal framework for AI in healthcare under discussion
In September 2023, Senate majority leader Chuck Schumer organized a meeting to discuss regulations to monitor the AI industry. Senators, heads of leading technology companies, and civil society leaders attended the first of nine sessions aimed at developing a consensus on draft legislation. He said: “Innovation must apply to both sides of the equation, innovating so we can move the advantages of AI forward, but innovating so we can deal with the problems that AI might create and lessen them as much as we can.”
All the attendees reportedly agreed that there is a need for the federal government to oversee AI, though the details are yet to be worked out.
The White house has meanwhile directed the Department of Health and Human Services to create an AI task force to create a strategic plan for deployment that balances innovation with risk mitigation. It seeks to:
- Enforce existing consumer protection laws and principles and enact appropriate safeguards against fraud, unintended bias, discrimination, infringements on privacy, and other harms from AI.
- Ensure the safe, responsible deployment and use of AI in the healthcare, public health, and human services sectors.
- Develop an AI assurance policy — to evaluate important aspects of the performance of AI-enabled healthcare tools.
- Create infrastructure to enable pre-market assessment and post-market oversight of AI-enabled healthcare-technology algorithmic system performance against real-world data.
- Create a framework to Identify and capture clinical errors resulting from AI deployed in healthcare settings.
- Develop a set of best practices to avoid harm and disseminate it among stakeholders.
- Prioritize grant-making and other awards to advance responsible AI innovation by healthcare technology developers, including AI tools that:
-Develop personalized immune-response profiles for patients.
-Improve the quality of veterans’ healthcare.
-Aid clinical care, real-world evidence, programs, population, and public health.
CONCLUSION
Practice safe and responsible AI in healthcare
The future of AI in healthcare is full of potential, and policymakers are taking the right steps to encourage innovation with caution. Healthcare companies have stepped forward to sign a White House pledge to practice responsible AI deployment. The FDA has also taken a keen interest in the application of AI and ML in drug development -focusing on crucial issues such as human-led governance, data quality, and model development standards. The American Medical Association has issued some guidelines for the development, deployment, and use of augmented intelligence, stressing the role of the physician: “The physician community should help guide the development of AI tools in a way that best meets both physician and patient needs, and help define their own organization’s risk tolerance, particularly where AI impacts direct patient care.”
In all, a risk-based approach underlining the importance of accountability and transparency seems to be the way ahead for AI in healthcare.
