New solutions with AI and machine learning are developing for challenges that once seemed insurmountable.
The future of health care is data-driven, bolstered by analytics and fueled by artificial intelligence (AI) and machine learning (ML). And with technologies like generative AI filling headlines, they promise to significantly enhance care, with new connections among primary care physicians, their patients, and other clinicians and specialists.
Combining the predictive power of analytics with the computing power of AI can improve patient outcomes and health equity and even secure the holy grail of interoperability, where different health care systems and platforms connect, collaborate, and share data seamlessly.
But, before enterprises across the ecosystem can capitalize on AI and analytics and realize interconnected care, they need a solid vision alongside solid data and analytics foundations.
The complexity of the health care industry brings with it significant, long-held challenges:
These challenges have seemed insurmountable. But now, with technology advances, there are finally effective solutions.
Leading health care enterprises are already harnessing data analytics to improve care delivery and outcomes. For example, by using analytics to understand the population, one provider introduced a range of initiatives that reduced emergency department usage by 5%. This included opening walk-in visits, expanding online urgent care, and redefining protocols for phone-based nurse triage.
By using analytics and AI to sharpen their decision-making, payers, providers, and clinicians are reducing costs and improving patient experiences. Ken McLaren, partner for data and AI at private equity firm Frazier Healthcare Partners, highlights how one of its portfolio companies, Caravel Autism Health, is using these advanced technologies. “Caravel can now predict what clinical activities will lead to improved outcomes in children with autism so that clinicians can better target and prioritize care. AI has helped increase the company’s ability to identify clients in need of additional treatment, triage and provide care more precisely, and improve overall outcomes as measured by standard industry measures (Vineland-3 and VB-MAPP). The impact also extends to employees who now spend their time more effectively.”
Other health care providers are improving care access and provision by using AI to predict which patients won’t attend their appointments at certain clinics. This helps improve capacity by overbooking appointments where no-shows are most likely and gives patients more opportunities to be seen and treated.
And with the rapid rise of generative AI, companies are using it to enhance clinical assistants and automatically generate medical documents from consultations, as well as improve their services through translation and communication solutions.
AI and analytics enable practitioners to quickly access patient insights, empowering them to take informed actions that impact revenue cycle management, clinical decisions, and, ultimately, population health management. But AI isn’t just about technology. It’s helping to bring humanity back to health care.
“The AI isn’t making clinical decisions, but educating and informing clinicians with insights that help them make better decisions, faster,” says McLaren. “With best-in-class data analytics, companies can solve major business problems, whether that’s improving how a clinician assesses a patient or ensuring oncology drugs are delivered to the right place at the right time while minimizing the risks of adverse effects or non-adherence.”
With so much potential, what’s stopping health care enterprises from fully embracing AI and analytics?
The industry is data rich, but that data is not being used effectively. For example, in a recent study, we found that access to data is one of the biggest challenges holding back health plan executives that are offering, planning to, or considering offering a range of social determinants of health (SDOH) benefits.
Data is an obstacle for several reasons:
The interoperability challenge continues to hurt the industry and patients. But the companies that are addressing it are making a difference. They’re improving patient services by sharing data across payers, providers, pharmacies, and beyond to identify population and socioeconomic trends. If they can predict an increase in kidney disease, for example, in one location they can hire the right physicians and invest in the right services to match future demand.
Investing in strong data foundations requires data lakes to create centralized repositories for structured and unstructured data. Then enterprises can collect, store, manage, and analyze vast volumes of data from myriad sources.
While collecting data is vital, so too is curating and cleaning it. After all, inaccurate or incomplete data can have serious consequences for patients, payers, and providers. It’s only with clean and well-curated data that enterprises can generate accurate health predictions to identify at-risk patients and proactively treat them for improved long-term outlooks. This is also vital for maintaining patient safety and reducing errors in administration, diagnosis, and treatment. Not to mention improving operational efficiencies for both payers and providers.
Combining data curation and cleaning with collection and storage in a data lake enables health care enterprises to unlock innovation in research and development while complying with privacy and security requirements. Companies can then create new capabilities, applications, and services by using powerful analytics and AI technologies, including generative AI.
Organizations are creating increasingly complex, vertically integrated systems that combine different elements of the health care ecosystem, such as care provision with health insurance with pharmacy, retail, and diagnostics. The ambition is to save downstream costs by better predicting and understanding SDOH and population health needs. But without strong data, engineering, analytics, and AI tools to model and visualize outcomes then companies won’t see the desired return on investment.
With data foundations in place, the ability to make interoperability and interconnected care real gets closer. Companies that can easily access and cross-reference medical records, test results, and other clinical data will gain a full view of their members and patients. This means they can predict if people are at risk from certain diseases, spot care gaps, and understand that what they need isn’t more care but perhaps financial help.
The organizations investing in their data foundations will be the ones that can use the most impactful technologies today and in the future. And with AI and analytics capabilities advancing at speed, we can expect health care tomorrow to be seamlessly connected, more personal, and better for all.
Alex Kleinman is global segment leader for the health care practice at Genpact, where he oversees the teams that provide digital transformation and managed services to major health care payers, providers, distribution companies, and health technology and services companies worldwide.
Supreet Arora leads Genpact’s health care data and analytics practice. With more than 15 years of experience in the health care industry, he leverages his domain and data and analytics expertise to enable innovative solutions for the clients across the health care value chain.