Paul Grundy

Dr. Paul Grundy is the Chief Transformation Officer at Innovaccer. Affectionately known as the ‘Godfather’ of the Patient-Centered Medical Home model, he is the Founding President of the Patient-Centered Primary Care Collaborative (PCPCC) and a member of the National Academy of Medicine. Prior to Innovaccer Dr. Grundy enjoyed an esteemed career at IBM, as the Chief Medical Officer of the Healthcare Life Sciences division, and the Global Director of Healthcare Transformation. Previous to IBM he was the Minister Consular for the US Department of State. Dr. Grundy is a globally respected healthcare convener, storyteller, and visionary - a "Trusted Healer.” He holds numerous international honorary titles and awards, including an honorary lifetime member of the UK National Association of Primary Care, an Ambassador for Healthcare DENMARK and the first international member of the Irish National Association of General Practice. He was the recipient of the NCQA National Quality Award in 2012, and recipient of the prestigious Barbara Starfield Primary Care Leadership Award in 2016.

I have always been intrigued by weather forecasts. Once while I was going through the newsfeed on my phone, 1 specific line caught my attention: “There’s 56% probability that it might rain today.” That was my awe-struck moment.

Such statements not just influence the way people think, but also their future course of action. That particular statement above from the weather department definitely made me re-plan my Taco Tuesday!

However, it made me wonder, “Can we make such claims in healthcare? Can we predict such outcomes for patients? Can we ever say to a patient you have a 30% probability of getting a heart attack in the next 3 months?”

What can enable us to find these answers? Artificial intelligence (AI)! Let’s dive into the depths of exploring this.

We have everything, don’t we? Then why are we failing?

When I look into this situation, I think that we, as healthcare stakeholders, are just living under the impression that we have everything. Actually, we are still working in silos. Success in the value-based care environment cannot be achieved based solely on clinical insights.

Social determinants of health (SDOH), factors that are responsible for 80% of health outcomes, are still a mystery for a lot of us. Although numerous research articles demonstrate that SDOH substantially contribute to a patient’s health condition and well-being, major complications in understanding them include:

How do we address these complex challenges?

Who is the best-positioned stakeholder to do so in a clinical environment?

We need to eliminate this data crunch situation. Healthcare as an industry generates mounds of critical data, but our inability to utilize it and make the most out of it serves as a crucial problem.

What is the right way in addressing SDOH?

Well, this article actually fuels up from here! AI, machine learning (ML), and predictive analytics—this trio holds the answer to most of the problems in healthcare.

Working with SDOH requires a thorough, drilled-down approach and the detailed use of predictive analytics to measure the at-risk population accurately and advance preventive care methods in a care ecosystem.

The effects of social determinants vary in accordance with a very small region. There is a high possibility that all the zip codes in a county will have different susceptibility to a particular social determinant. The best way to address SDOH is to start the analysis from the national level and drill it down to the zip code level to efficiently measure the impact of every social factor accurately and precisely.

We now have the data. What’s next? We are still nowhere close to predicting the care journey of patients.

It all starts with the right data. The data from such sources including the clinical data, claims data, and SDOH, among many, allows us to gain a comprehensive view of the patient’s care history and apply ML algorithms to predict the future.

Imagine what we could have achieved if we have both:

Right clinical and claims data about the patient

The dollar amount that a patient might spend on the illness

A major role is played by "time" in such analysis. For predicting the right outcomes in the upcoming year, we need to make sure that we know what has already happened to the patient in the past. The predictive model should take the patient’s past years’ cost, utilization, and diagnosis data into consideration to understand the patient’s health in depth.

Once we have all these handy, just 1 final ingredient is left, which is the inflation rate. Not just the financial aspect but some demographic adjustment is also required to understand the cost of care that will be incurred for a patient in the upcoming year.

What new innovation can we bring to the table with this approach?

Once the organizations are able to leverage these advanced analytics and AI tools, they can easily stratify their patients and identify the ones who are in dire need of immediate care interventions and can make the cost-cutting process more streamlined. Additionally, proper insights into the non-clinical data sets such as the SDOH data can assist the care teams to overcome the adverse outcomes of the citizens of that region.

The analysis of social determinants can be applied for multiple use cases such as:

Identifying the role of behavioral health providers, social workers, and health coaches;

Increasing the efficiency of the care coordination team;

Forging better partnerships with community resources and social improvement funding agencies, and many more.

What does the future with AI hold for us?

Most people see technology as the replacement of humans. Wrong! They can never be the replacements; they are the support systems. We are stepping into the age of predicting and preventing diseases instead of curing them. That was the traditional approach, with non-clinical data and resources such as SDOH, we can change the future of US healthcare. From assisting providers to use data-driven insights to target patients, to projecting the cost of care-predictive analytics, this can change the dynamics of healthcare. The future with AI and ML is here, all we need is to just embrace it.

Articles

The Future of Care Management in the Age of Artificial Intelligence

There are intrinsically 3 parameters for a care delivery system to become a value-based model: delivering the right care, at the right time, and at the right price. However, simple as it may sound, there are many challenges that stand in the way of delivering perfect care. These impediments end up being directly or indirectly responsible for most of the complications that our healthcare world faces.

Problems Associated With Conventional Care Systems

Patient-centered care is a relatively new concept in the healthcare space. Although concepts such as the personalized, consumer-first approach have been around for a while, going full throttle with a focus on patient needs is a new phenomenon. Traditionally, healthcare revolved mainly around providers.

This approach was successful back then, but now that value-based care has pervaded the healthcare world, the approach of healthcare stakeholders has to change. Non-personalized care models can ruin the purpose behind developing a model that focuses on wellness instead of just curing diseases.

The factors associated with healthcare remuneration does not incentivize providers to concentrate on preventative measures as much as treating the patients when they have contracted diseases. Conventional care procedures assume more importance than the personalized care models. Apart from misplaced incentives, providers have also faced a dearth of support to focus on prevention.

What’s interesting is that the healthcare sector has been more devoted to addressing the problems rather than analyzing the reasons that might have caused the problems in the first place. Organizations don’t have access to detailed insights into patient choices, and the right resources to leverage the information that can enhance the care system. Unfortunately, healthcare has stayed behind in adopting state-of-the-art technologies that can be of great assistance to care teams in improving care delivery. Many organizations are dependent on outdated information in their systems and fax machines even now.

Another attribute that has been hampering healthcare’s progress is the inability to adopt a team-based approach. Providers more often than not are absorbed into multiple menial tasks that may not boost the cause of making patients healthier. They are often caught up in intricate data files and labor-intensive tasks that do not qualify as delivering care. For instance, physicians should not have to make manual entries and care coordinators should not have to devote time to making dozens of calls to follow up with the patients.

A Fresh Perspective on Provider-Patient Dynamics

Patients demanding a consumer system, such as a care delivery model, is rising. They want to be included in the decision-making processes on top of getting personalized services. Consequently, organizations aspiring to stay at the top of the game are taking initiatives to come up with customized solutions. This prospect is promising; however, the healthcare system still has a long way to go.

Keeping in mind that patients’ needs are important but, under no circumstance should they override the decisions of care teams. They are important cogs in the healthcare wheel, and one of the primary reasons why the sector has not been making a smooth transition is due to ignoring the needs of physicians and care teams. Unavailability of supporting elements, including data insights and increasing administrative burdens on them, is impeding healthcare’s efficiency.

The Institute for Healthcare Improvement Triple Aim expanded to become the Quadruple Aim. Quadruple Aim is a relatively new term for recognizing the significance of physician engagement. To build a quality healthcare system, it is important to focus on the needs of physicians. Complete patient satisfaction can be achieved only when physicians are happy.

Amalgamating payments with performance is a great concept as long as physicians get to fulfill their objectives. We need more humane physician leaders who have an understanding of payment models and meaningful use of data.

Endowing Primary Care Providers (PCPs) With the Support of the Data Activation Technology

To build a sustainable patient-centered healthcare system, primary care will be a crucial element. PCPs and their teams should take initiatives to enhance the future health of their patients. Activating the entire healthcare data for providing physicians the right insights at the right time can substantially improve healthcare outcomes.

The biggest mischief-makers of the healthcare space are excessive utilization and high costs. PCPs are the sole stakeholders of the healthcare system that can eliminate these problems. The framework and policies necessary to achieve the objectives of patient-centered healthcare system can be put to work only by the PCPs.

I have seen organizations that have reduced their emergency department visits by monitoring at-risk population or diminished annual visits by increasing Annual Wellness Visits. I personally think that an organization’s intent matters more than anything else. When the care teams have the required resources like accurate data points in place, they can encourage more patients to visit primary care facilities.

Healthcare visionaries will agree that it is important to invest in the right technologies to boost care and quality outcomes. After identifying the cause of healthcare obstacles, they must be on a lookout for technological solutions to overcome them. Providers need insights to power clinical applications while delivering care, without searching for them elsewhere.

Need for Data Activation and Artificial Intelligence (AI)

Physicians are ready to drive healthcare change rather than being part of the problem. AI and machine learning are going to provide them with the support that they need to deliver care that patients desire.

There are so many sectors utilizing AI to improve customer experience. Healthcare, however, is falling way behind in this trend. Physicians need to go back to their electronic health records to find relevant patient details. There lies a huge opportunity for improving healthcare operations.

Leveraging the benefits of “activated data” can bring the much-needed developments that healthcare needs. Aggregating data is the key to ensuring smooth implementation of patient centered care. For instance, ideally patients with chronic illness should be informed about their care plans in real time and should receive reminders to manage their condition better. This information can also reduce the care episodes dramatically, and AI can help organizations to make this scenario come true.

Falling behind in adopting technologies that make processes easier and simpler causes organizations to leave money on the table. By not allowing their care teams to see more patients on a daily basis, owing to the lack of time, they are losing out on the opportunity to improve healthcare. The sooner they innovate, the sooner will they achieve the Quadruple Aim of Healthcare.

There are intrinsically 3 parameters for a care delivery system to become a value-based model: delivering the right care, at the right time, and at the right price.

Author | Paul Grundy

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