A Treatment Plan for Improved Healthcare: Make the Most of Your Data through AI and Analytics
Disruption isn’t just limited to the consumer tech space – it’s impacting many different industries. The healthcare sector has not been immune and is amid a significant period of transformation with the increased influence of artificial intelligence (AI) and data analytics.
Across society, companies and individuals rely on data and expect it to flow seamlessly. In the healthcare setting, patients expect the same instantaneous and interconnected data flow they are seeing in other areas of their life. But to make that happen, practitioner buy-in, regulatory direction and financial investment is crucial for moving the needle to widespread adoption of AI in healthcare.
Healthcare organizations are also managing tremendous amounts of data, not just related to patient care and outcomes, but to labor management, facility operations, accounts payable and receivable, insurance coding and billing, prescription drug management, and customer satisfaction. By managing this data flow with technology and innovation, healthcare organizations can reduce costs and ultimately provide better patient care.
Data Sets and AI in Patient Care and Outcomes
The medical community has been slow to accept AI even as it has proliferated in other industries, but the technology has great potential to drive greater accuracy in healthcare outcomes. Using AI, healthcare organizations can create more complete data sets and apply algorithms that can be used to identify patterns in diagnoses, symptoms and treatments.
In hospitals and physicians’ offices, data takes the form of electronic medical records (EMRs) or digital charts. The EMR aggregates data to make it more usable and transferable. The data from the EMR can be used on a patient level to ensure care given by different doctors and in different settings (regular offices visits, emergency room, diagnostic tests, etc.) is shared, and it can be used on a population-level to look at what symptoms are associated with diagnoses, comorbidities (diseases that tend to occur at the same time) and other useful patterns.
The Challenges to AI Adoption in the Healthcare Sector
Because of a risk- and change-averse culture, the time and financial investment it takes to train and implement data in workflow, and confusion around regulatory issues, AI in healthcare is not reaching its full potential.
Healthcare organizations are often focused on mitigating risk and relying on well-established approaches. The manual work put into ensuring that patient data is entered into the system can lead to human error – especially when back office employees are working long shifts or are not sufficiently trained. Inefficiencies or mistakes in data use can lead to misdiagnoses for patients, longer processes and even potential dangerous outcomes, such as drug contraindications or overdoses.
Data security and ownership is another major issue for healthcare providers, who must ensure their processes are compliant with the Health Insurance Portability and Accountability Act and the 21st Century Cures Act. The rules around data are so complex that some health systems are unsure what they can and cannot do, and thus are simply holding off on employing AI and other data technology.
Healthcare providers are also underusing their data sets – they’re collecting and storing tremendous amounts of information, but not analyzing it and putting it to work to create change. These data sets, when used in conjunction with analytics, give physicians the opportunity to leverage the information to lead advancements in healthcare and gain a competitive edge amongst providers.
Treating Healthcare Inefficiencies through AI
Combining AI with existing systems and pairing physicians and other caregivers with the technology can create significant change in healthcare – both in better serving patient needs and improving operations practices. With a more streamlined approach to capturing data and analyzing it through AI and machine learning, healthcare experts have a better chance at ensuring patients receive the right care and treatment.
Another benefit of AI in healthcare data is that it ultimately reduces costs for healthcare organizations. As physicians become more acclimated to using the technology, fewer inaccuracies and more patient proactivity occurs, giving the physician a better chance at getting the diagnoses right the first time and moving from treatment to prevention management with the patient.
On the operations side, AI and business information (BI) can make time- and staff-intensive processes such as hiring, facilities management and maintenance, billing and payments, labor management, and customer feedback and change management much faster and less costly. Many other corporations have embraced AI for similar processes, but healthcare has yet to achieve this cultural shift.
Investing in the Future of Healthcare Technology
Implementing AI in healthcare can take significant investment, but the returns –– in the form of healthier, satisfied patients and time- and resource-savings –– can be exponential. As AI becomes ever more sophisticated, it has the potential to reshape the healthcare and biotechnology industries and lead to advancements in treatment and disease prevention and cures.
In conjunction with this growth, Key Healthcare is creating continuous opportunity by partnering with healthcare organizations to educate about, invest in and implement the latest technology, including AI, machine learning and data analytics.
Just as looking at the whole health picture of a patient can help improve outcomes, so too can healthcare organizations take a holistic approach to their finances and operations. Key Healthcare brings together a national team of experts in Healthcare Banking, Healthcare Real Estate and Healthcare Investment Banking. They understand the disruption the industry is facing today and how embracing it can prepare your organization for the future.
- Healthcare organizations manage a tremendous amount of data but have been slow to adopt artificial intelligence and machine learning to analyze and put it to use.
- Better use of healthcare data can improve patient outcomes, create healthcare advancements and save healthcare organizations time and money.
- AI in healthcare is hindered by risk aversion, reluctance to invest resources and time, and the complexity of regulations in the healthcare sector.
- Investment in healthcare technology, such as AI and machine learning, can create positive returns for patients and providers.
To learn more, connect with Don Hooker, Senior Equity Analyst, KeyBanc Capital Markets at email@example.com or (917) 368-2378.