In a rapidly evolving healthcare environment, AI has the potential to transform the way consumers interact with healthcare services. Today, U.S. consumers struggle with everything from finding the right insurance to understanding when they need to see a doctor, how much it costs, and how to manage their health. I'm doing it. Many people spend hours researching, talking to friends and family, and calling their providers and payers to find answers to their questions. In fact, one in four consumers we surveyed say they can't get the care they need when they need it.
Meaningful improvements in addressing complex healthcare ecosystems require more than just increasing human involvement, especially given workforce shortages and rising healthcare costs. Enter AI: This technology has the potential to reimagine consumer experience (CX) and enhance engagement in ways that were not possible just a few years ago. This helps enable personalized care, increases transparency and simplicity, and ensures consumers are in control of their health and healthcare-related decisions. In a recent study in which medical professionals rated doctors' responses to questions from patients on social media forums compared to responses generated by chatbots, raters preferred the AI responses and found them to be of higher quality and more It was found that they were rated as empathetic.
The good news is that the healthcare industry understands the opportunity for AI in CX. In a McKinsey survey of healthcare leaders, 62% of respondents said consumer engagement and experiences are the areas where generative AI (Gen AI) has the greatest potential. However, only 29% of respondents have started implementing genetic AI within their organization for any purpose.
Organizations that use AI to tailor healthcare experiences to individual needs, preferences, and goals while mitigating potential risks may benefit from increased trust with consumers. . Implementing emerging technologies such as AI can also improve business outcomes. One study estimates that net savings can be 5% to 10% of health care costs, although the exact percentage varies slightly by private and public payer, physician group, and hospital. The study was co-authored with McKinsey & Co. and published by the National Bureau of Economic Research.
Why healthcare consumers need AI now
Successful use of AI requires data, and the industry has enough data for AI tools to interrogate, with around 30% of the world's data being generated by the healthcare industry. The compound annual growth rate of health data is expected to reach 36% by 2025. Health care organizations also have a unique set of consumers who are more willing to share personal health-related information to support their health than employers, governments, and technology companies.
However, the healthcare sector typically lags behind other sectors in adopting digital technologies. To date, apart from patient privacy concerns being paramount, the main technology challenges in leveraging AI have been the inability to extract valuable insights from unstructured data and the highly diverse consumer behaviors. The inability to curate a tailored experience. Like other industries, data is spread across multiple systems, and while the healthcare industry uses some of its structured data for AI, it has had less success with unstructured sources such as call records. No. Now, Gen AI can unlock the power of previously unusable data sources containing critical consumer information, making it compatible with the widespread use of AI to learn behavioral patterns, and previously It offers new capabilities that provide customization on a scale previously unattainable.
Technology is advancing at lightning speed and has the ability to help optimize the healthcare consumer journey.
How AI can streamline efforts across healthcare
AI has the potential to revolutionize the entire consumer healthcare journey. Consumers experience care across several key journeys. These steps don't have to be chronological, individual, or the same for each individual, but each stage provides an opportunity for AI to improve the experience.
How can we accelerate the use of AI to improve consumer satisfaction?
Despite the potential of AI to improve end-to-end consumer healthcare efforts, its momentum is slowing. Organizations are caught between excitement to seize the opportunity early and a lack of alignment on where to start, alongside general vigilance considering the potential risks associated with AI adoption. Masu. To accelerate the use of AI and upgrade the consumer experience, we suggest five key steps.
Tackle 70% of data preparation problems
First, executives should consider their organization's data and technology readiness before allocating resources and funding. Delivering tangible value to healthcare consumers through AI requires readily available, integrated data. This is a difficult task that accounts for an average of 70% of the work when developing AI-based solutions. The challenge for healthcare is knowing what data to collect and how to connect those sources. Data spans multiple platforms and is fragmented in different formats and levels of utility (e.g., billing and electronic health records hosted on-premises, marketing information on cloud platforms, distributed across multiple systems, etc.). call center information, etc.).
And while healthcare organizations may have a data advantage compared to other sectors, they still face gaps that prevent them from gaining a comprehensive view of their consumers. For example, interruptions in treatment continuity make it difficult to fully understand a patient's needs, habits, and preferences. AI output can also be biased unless it is built on demographically diverse data. Organizations can also supplement clinical and patient data with information about social determinants of health, patient-reported outcomes, retail purchases, and health trackers to uncover meaningful insights.
Focus on consumer experience priorities to ensure AI success
Parallel to assessing data readiness, leaders should assess and prioritize areas for AI investment based on importance of CX, opportunity, strategy, and overall priorities to improve feasibility. can be attached. For example, AI can optimize administrative processes to reduce consumer touchpoints and reduce the cost of services. For providers, it could mean fewer cancellations as a result of a better overall experience, but for payers, it could mean fewer follow-up calls to answer questions about benefits and coverage. There is a gender.
This is an important step to avoid trying to do too many things at once, as this can limit meaningful progress. It is essential to involve cross-functional leaders within the organization to prioritize areas of focus. For example, clinical leaders in particular have first-hand insight into patient pain points and what exactly is going wrong in care delivery and CX.
Optimize real-time insights for AI-powered interventions
Once the data foundation is established and priorities are set, organizations can begin to understand what else is needed to properly contextualize the data they collect. Delivering truly personalized, AI-powered insights requires connecting multiple touchpoints across data sources to build personalized consumer journeys. AI models can more accurately represent key consumer behaviors by combining details such as doctor visits (frequency, type of visit, location of appointment), patient outreach, and patient interactions and experiences. It will look like this. Build predictive analytics to inform future interventions.
By analyzing details such as patient appointment preferences and when and how they respond to outreach, AI adjusts timing, frequency, and message themes to make recommendations that are most likely to resonate. can be provided. Gen AI can further increase the effectiveness of these timed interventions using hyper-personalized message content.
Map AI risks in healthcare and create mitigation plans
Compared to other industries, healthcare leaders face unique challenges considering consent requirements, privacy risks, potential health impacts, regulatory oversight, and more. Organizations can use some consumer data because consent mechanisms are in place when members register or schedule reservations, but there is an easy way for consumers to confirm or adjust these consents. there is no. They should be able to learn not only about data usage when signing a new consent form, but also about any changes to the privacy policy regarding previously provided consents, with clear opt-out steps.
In addition to data use transparency, organizations can establish governance processes that also focus on AI use and algorithm transparency. You can provide consumers with clear logs and documentation about your AI systems. This includes bias reduction strategies and training protocols, such as details of the population profile used. As in other industries, as consumer expectations shift toward easier access and control over data, for example, allowing customers to decide which purchases are used to train ML recommendation models. Some companies are now taking similar measures, but medical institutions will be under increasing pressure to do the same. .
Adding to the already complex patient privacy landscape, AI-specific regulations are rapidly evolving. The Department of Health and Human Services' AI Task Force is developing policies to protect patients as part of the White House's executive order on AI safety. Finally, more mature, unified data repositories built to power AI may become valuable targets for cyberattacks. 2023 marked a new record for health data breaches, with approximately 725 recorded breaches of more than 500 records, more than double what was reported in 2017.
Level up your team's AI capabilities
In the long term, provider organizations and payers will need to invest in their capabilities and talent to fully capture the AI opportunity. You need to carefully balance upskilling existing talent and recruiting AI-specific skills, and assemble tactical teams to act on the initiatives you select. Partnering with a third-party AI vendor is also an option, which can allow organizations to move quickly.
One way to increase the likelihood of successful AI implementation is to adopt a co-pilot model. In the co-pilot model, employees collaborate with AI tools to improve processes incrementally. It leverages the speed and power of AI and the checks and balances of human skill and intuition to reduce errors and risk. Importantly, this process includes a period of functional testing and learning among a small set of users before expanding across the enterprise. Such test-and-learn tactics allow organizations to avoid scaling risks and measure impact and adoption within existing workflows.
Currently, interactions with the healthcare ecosystem are often unwieldy and lack the personalization that consumers expect. AI has the potential to reshape healthcare by enabling consumer centricity. Building scalable AI solutions requires an iterative approach, a defined and controlled launch strategy with a clear plan for how to integrate with existing or reimagined workflows, and a well-functioning You need key performance metrics to scale what you have. Executive involvement is also key to harnessing the power of the flywheel effect. Enabling this revolution will require targeted investments, advances in data, and reduced risk, and we are hopeful that our efforts will pay off. Implementing healthcare AI can benefit an organization's bottom line as well as operational and administrative functions, allowing consumers to enjoy a greater sense of responsibility for their health and wellness journey and improve their overall health. You can.
