Enterprise-level healthcare is a massive business sector with seemingly infinite moving parts. With patients’ lives at stake, liability is huge, leaving little room for error. Yet human error is common in healthcare, and minor mistakes can cost millions in legal fees and lawsuits, not to mention risks to patient health. Healthcare enterprises are quickly embracing artificial intelligence solutions to mitigate loss, streamline operations, improve efficiency, and enhance customer service.
Learn about the latest trends and solutions in healthcare AI, obstacles to AI adoption, and how artificial intelligence is rapidly transforming standards of patient care in 2023.
#1 Personalized Data-Driven Healthcare
The prevalent one-size-fits-all approach to patient diagnosis and treatment has serious flaws that can potentially do more harm than good. Every patient has a unique biological makeup influenced by unique genetic and lifestyle factors. Many have comorbidities and are being treated with multiple medications that can be incompatible with additional condition-based prescriptions. Symptoms-based diagnosis and cookie-cutter treatment solutions can be costly for both patient and healthcare provider, undermining patient health and increasing liability risks for doctors.
Personalized integrative healthcare that treats the whole patient and not just their symptoms is perhaps the most important breakthrough trend in healthcare. Artificial Intelligence can play a critical role in collating key data that gives doctors a more in-depth and comprehensive patient profile for customized patient care. A well-trained machine learning (ML) model can do predictive diagnostics based on patient data and make recommendations for long-term treatment.
#2 Automated Document Processing and Management
The tightly regulated nature of enterprise-level healthcare demands massive documentation to remain industry-compliant and offset liability. Cloud-based record keeping has been a godsend, but processing mounds of paper documents remains a significant and growing challenge.
Automated document processing powered by AI can be a game-changer that eliminates errors, improves efficiency, and downsizes staffing needs in multiple routine operations. AI solutions with a document processing component can be used to:
- Centralize and standardize healthcare data from different departments
- Manage patient flow
- Manage inventory and recommend predictive purchasing
- Automate medical coding and billing
- Process and analyze patient electronic health records (EHRs)
- Perform mundane data entry tasks
- Update and retrieve patient charts
- Assist in appointment scheduling
- Efficiently process insurance claims
- Automate staff scheduling and payroll
- Perform a multitude of other routine operational tasks
#3 Medical Diagnostics
Accurate diagnosis is critical to effective treatment. Artificial intelligence provides doctors with advanced image-interpreting tools for detecting anomalies that are invisible to the human eye. AI can help reduce the risk of human error by analyzing CT scans and retinal images, reading electrocardiograms, detecting cancer cells, and flagging neurodegenerative disorders in their early stages.
Data-driven predictive diagnostics can help doctors and patients prepare long-term healthcare strategies that optimize the patient’s quality of life. AI can dramatically reduce the need for unnecessary testing and protect patients from undergoing excessive or inappropriate procedures.
# 4 Patient Data Analytics
Machine learning algorithms can process large quantities of medical data from patient EHRs, medical devices, and wearable monitors for early disease detection and alerts for rapid intervention. Predictive models can analyze patient data to identify stroke risks, cardiovascular disorders, neurodegenerative conditions, and more. AI-assisted software can analyze respiratory and cardiac acoustic data to flag irregularities and alert physicians to potential problems.
# 5 Surgical Robotics
Robots bring AI into the operating theater to enhance precision and perform complex procedures. Surgical robots help to reduce the risk of complications and increase successful outcomes by fine-honing invasive procedures. Robotic ML models can be trained to identify and assemble surgical tools for specific procedures, and to automate equipment cleanup. Robots can even operate remotely with surgeon oversight to perform interventions in isolated regions or on the battlefield.
There are many more ways that healthcare entities can leverage artificial intelligence to improve patient care, reduce operating costs, optimize efficiency and enhance working conditions for medical staff. An experienced AI consultant can help you identify AI’s potential uses for your organization.
Obstacles to Healthcare AI Adoption
Healthcare AI is still just starting, and we expect to see a boom in innovations as more healthcare entities adopt AI solutions. But while the benefits to be reaped from healthcare AI speak for themselves, broad-scale adoption poses many challenges:
Eliciting stakeholder buy-in is a huge hurdle
Many stakeholders are skeptical of new technologies like AI and reluctant to integrate them into existing healthcare systems. Moreover, the cost of custom solutions development, hardware and software, consultation fees, specialized staff onboarding, and ongoing system maintenance can seem prohibitive to budget-conscious administrators. Therefore, selling the concept of AI to administrators and board members may be your biggest hurdle to healthcare AI adoption.
Prioritizing AI projects takes time
AI implementation is by no means a quick fix. Identifying the areas in your organization where AI can be most impactful is essential, and prioritizing those areas according to budgetary constraints, urgency, or stakeholder support. To start, try to zero in on a single area where the benefits of AI will be most apparent. Conspicuous success in one area will pave the way for the adoption of future AI projects.
Integration with existing systems is complicated
The compatibility of AI solutions and ML models with existing systems varies from one system to the next. This is where early consulting with experienced AI professionals pays off. Getting a realistic picture of the feasibility and cost of AI integration will streamline the adoption process and eliminate unexpected costs down the road.
System failures can be devastating
“If you fail to plan, plan to fail” is an appropriate adage for implementing AI. Here again, working with a professional AI consultancy is key. An experienced AI team will ensure you tick all the boxes, from employee training to system maintenance and upgrades.
Adequate tech support is crucial
Developing and implementing your AI solution is just the tip of the iceberg. Like other new technologies, AI is rapidly evolving, and you will need ongoing maintenance to fix unexpected bugs, make frequent upgrades, and evaluate model performance. You can onboard an in-house AI team or partner with an established AI company to keep your systems up-to-speed and humming along.
Getting Started with AI for Healthcare
Careful planning and informed decision-making are key to launching any AI solution. After getting the green light from stakeholders, your first step should be to consult with an experienced AI team with a proven track record in developing healthcare AI models. Given the relative newness of AI, you may have to dig deep to find a reputable company with enough experience and expertise to assess your specific needs and build customized solutions to meet them.
AI transformation is a process, not a one-off event. Once you identify the key areas where you want to implement AI, choose one or two small projects to get the ball rolling. Starting small allows you to experiment and troubleshoot, exposing your staff to new concepts without overwhelming them. Small projects also lay the groundwork for ongoing collaboration with your AI providers, allowing them to showcase their expertise in building AI solutions that meet and exceed your expectations.
AI solutions in healthcare may still be in their infancy, but your biggest competitors are already adopting AI and reaping its rewards. So, if you want to remain competitive and scale your healthcare enterprise, it’s never too early to join the AI revolution in healthcare.
About Dmitrii Evstiukhin
Dmitrii Evstiukhin is the Director of Managed Services at Provectus. He leads a decorated team of experts who deliver cloud-based solutions to Provectus’ clients and partners. Before joining the Managed Services team, he held a Senior Solutions Architect position and was responsible for designing, building, and implementing advanced solutions in the cloud. Dmitrii is passionate about leveraging the latest technologies, encompassing cloud, data, AI/ML, and analytics, to help businesses achieve their goals.