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In a recent webinar co-hosted with AMGA, value-based healthcare leaders shared actionable insights on how AI can help organizations navigate these challenges and enhance value-based performance. Below are eight critical insights from their discussion.
Master the details of value-based care contracts
Success in value-based care begins with a comprehensive understanding of contract details. Many organizations stumble by overlooking critical nuances–such as how risk adjustment is calculated, which quality metrics determine bonuses or penalties, and other fine print that can drastically affect financial outcomes. Jonathan Meyers, CEO of Seldon Health Advisors, a Healthcare Management and Actuarial Executive with over three decades of experience, emphasized that missing even a small detail in these agreements can lead to unwelcome surprises. “Making sure you understand the entire agreement is critical. If you miss certain pieces, the arrangement could quickly go sideways,” he said.
In practice, this means healthcare leaders must dig into the specifics of every value-based contract and ensure all teams (clinical, financial, IT) understand their responsibilities.
Key contract elements to examine include:
- Risk adjustment methodology: How patient complexity is quantified and how often scores are updated – inaccurate coding and delayed documentation can directly reduce revenue.
- Quality and performance benchmarks: The exact metrics (e.g. readmission rates, preventive care measures) that impact incentives or penalties, and the thresholds that must be met.
- Attribution and patient population definitions: Which patients are included under the contract, how patients are assigned, and any exclusions. Misunderstanding attribution rules could mean focusing on the wrong population.
- Shared savings/losses formulas: The financial model defining how cost savings or overruns are split. Knowing if there are risk corridors or caps can inform budgeting and care management intensity.
- Data reporting requirements: Deadlines and formats for submitting quality data or claims–meeting these in a timely manner is essential.
By mastering these details, organizations can align their operations with contract goals. For example, if a contract heavily weights chronic disease management, a provider group might invest in care managers for those conditions. Thorough understanding of contract terms helps prevent unexpected shortfalls and ensures all stakeholders work toward the same targets, rather than scrambling mid-year to address overlooked requirements.
Prioritize high-impact initiatives and manage resources wisely
In value-based care, it’s impossible to tackle every improvement area at once. Limited resources–clinicians, staff time, and budget–force organizations to prioritize. Success comes from focusing on a few high-impact initiatives rather than spreading efforts too thin.
This requires closely evaluating team capacity, and concentrating on the initiatives that will most improve health and financial outcomes. Ron Rockwood, Executive Director of Value-Based Care at Jefferson City Medical Group, shared that his organization identified three focal areas for the coming year: enhancing patient experience, investing in employee experience, and improving care for specific high-risk conditions. By zeroing in on these objectives, they’re ensuring that they can make a measurable difference.
Organizations should convene leadership to review performance data and pick 2–3 priorities (e.g. reducing hospital readmissions, improving preventive care uptake, streamlining billing) that align with value-based incentives, and then channel resources into those areas. This disciplined focus prevents initiative overload and builds momentum in achieving key targets before expanding to the next challenge.
Timeliness is an important factor in resource allocation. “By the very nature of it, the doctors, the nurses, and all of the providers are a limited resource,” Meyers noted. “Making sure you're prioritizing the right things at the right time is critical.” This might mean using AI-driven risk stratification, discussed below, to direct care managers toward patients who will benefit most, or using technology to automate low-value tasks so staff can focus on care. By prioritizing initiatives and aligning resources accordingly, healthcare organizations can make sustainable progress in their value-based care goals.
Value-based care is rapidly becoming the preferred model for healthcare reimbursement, with a projected market growth from $12.2 billion in 2023 to $43.4 billion by 2031. This shift reflects a broad industry push to reward providers based on patient outcomes rather than service volume. However, value-based success requires a deep understanding of reimbursement models and effective risk management strategies.
Elevate employee experience to improve patient satisfaction
There is a direct line between employee (and clinician) satisfaction and patient satisfaction. Overburdened, burned-out providers cannot deliver their best care, and patients notice. As Rockwood emphasized, healthcare leaders should invest heavily in supporting their staff: “Take care of your staff, and they're going to take care of your patients. Employees come first because by doing that, everything else falls in place.”
An employee-first strategy is based on the understanding that a thriving workforce drives better patient experiences, quality, and loyalty. Consider the administrative and workflow burdens clinicians face, from clunky EHR systems to back-to-back appointments. Reducing these pain points improves morale and frees up energy for patient care. Rockwood shared how Jefferson City Medical Group introduced digital check-ins, automated appointment reminders, and real-time delay notifications for patients. These changes made life easier for front-line staff and physicians: patients “check in” by updating their information online ahead of the visit, and are automatically informed if the doctor is running late, relieving staff from having to manage those issues manually. The result is a smoother experience for patients and a less stressful workload for employees.
Other strategies to boost employee experience include ensuring adequate staffing levels, offering flexibility to prevent burnout, and soliciting regular feedback from staff on process improvements. The payoff for these investments is clear: happier staff leads to happier patients, ultimately improving patient retention and the organization’s reputation. In the context of value-based care, patient satisfaction scores (such as CAHPS) often factor into quality metrics, so raising them by caring for caregivers is both the right thing to do and the smart thing to do. Healthcare organizations should treat employee well-being as a core component of their value-based strategy, not an afterthought, knowing it underpins excellent patient-centered care.
Anticipate future risks with proactive stratification
Risk stratification–categorizing patients by their health risk levels to tailor care–is a crucial strategy when managing large patient populations. However, a common pitfall is relying too much on historical utilization instead of looking forward. “Many predictive models just chase last year's costs,” Meyers said. “The key is identifying patients for whom proactive care can prevent next year's exacerbations.” In other words, effective stratification must be dynamic and forward-looking.
To do this, organizations should leverage AI and advanced analytics on clinical data, not just retrospective claims. For example, an AI model might flag a patient with rising A1c levels and poor medication adherence as a likely future high-cost patient due to uncontrolled diabetes, even if they haven’t been hospitalized yet. Armed with this insight, care managers can reach out now, rather than waiting until the patient shows up in the ER. Rockwood echoed that risk is not static: a patient’s risk level changes with new behaviors or conditions, so stratification models must update continuously.
Proactive risk stratification empowers care teams to focus resources where they can make the biggest impact. Jefferson City Medical Group applied this approach and saw tangible results. By identifying subsets of chronic disease patients (for instance, those with diabetes or congestive heart failure) who were trending toward higher utilization, they provided extra support and frequent check-ins for those individuals. The result of these focused interventions was a 20% decrease in hospital readmissions for diabetic patients and a 15% reduction for chronic heart failure patients. This example illustrates how predicting and managing tomorrow’s risks, rather than reacting to yesterday’s events, improves outcomes and lowers costs.
To implement this strategy, organizations should ensure that their analytics tools incorporate real-time data and that care teams regularly review and act on risk reports. High-risk lists should be updated monthly or even weekly. Ultimately, the goal is to prevent health exacerbations before they happen–a primary goal of value-based care. AI can play a pivotal role by crunching vast data to find hidden patterns and emerging risks that clinicians might not spot on their own.
Develop targeted care programs for high-risk populations
Identifying high-risk patients is only half the battle–the next step is to develop targeted interventions that address their specific needs, for example by establishing specialized care programs or clinics for certain patient cohorts. Rockwood described how Jefferson City Medical Group opted to focus on patients with chronic obstructive pulmonary disease (COPD), a group with a historically high readmission rate. By assembling a dedicated multidisciplinary team to manage COPD patients proactively, they aim to reduce hospitalizations and complications for this population.
This disease-focused clinic approach can be replicated for other high-risk conditions like diabetes, heart failure, or frail elderly patients. The idea is to concentrate expertise and resources on a defined group to deliver intensive, coordinated care. A diabetes care program, for instance, might include monthly education classes, nutrition counseling, home glucose monitoring with telehealth check-ins, and fast-track clinic access if issues arise. Such targeted services go beyond routine primary care and can significantly improve outcomes for patients who need extra support.
AI tools are valuable in both identifying candidates for these programs and sustaining them. Predictive algorithms can sift through patient data to flag which individuals would benefit most from a specialized intervention (e.g. patients with multiple ER visits for asthma could be enrolled in an asthma management program). AI can also help program staff by aggregating each enrollee’s data, ensuring the team has a comprehensive view and can personalize interventions.
The benefit of targeted programs is evident in outcomes and efficiency. By focusing on a high-need segment, organizations can reduce utilization costs (fewer admissions and ER visits), improve quality metric performance (better disease control, higher adherence to care plans), and enhance patient satisfaction (patients feel supported by a team that really understands their condition). An important actionable step is to review population data and identify one or two conditions or patient segments that drive a large share of costs or have poor outcomes, and to then pilot a specialized initiative for them. As Rockwood noted, taking this focused approach in areas like COPD has strong potential to drive down avoidable utilization and provide more consistent, proactive care. These programs, supported by AI for data tracking and risk alerts, can become a cornerstone of value-based success for high-risk groups.
Integrate AI seamlessly to support clinicians
When deploying AI solutions in healthcare, workflow integration is the key to success. Even the most powerful AI tool will fail to deliver value if it is cumbersome for clinicians to use. Meyers observed that a major pitfall is expecting doctors or nurses to step outside of their normal workflow, such as clicking through multiple screens or logging into a separate system, to access AI-driven insights. Busy providers will understandably abandon a tool that disrupts patient care duties, negating any potential benefit of the technology. Ease of use and seamlessly embedding AI into existing clinical workflows are paramount.
Jefferson City Medical Group chose to use Navina’s clinical AI copilot, which integrates directly into the EHR. Navina’s alerts and recommendations appear in the same screen clinicians use for charting, so the decision support is in context and immediate. This seamless integration led to higher adoption rates, because providers didn’t have to alter their routine or hunt for information.
A seamlessly-integrated AI solution not only avoids burdening clinicians, it actively reduces their workload and burnout. Rockwood noted that prior to using Navina, physicians were spending hours after clinic or coming in early to prepare for patient visits, combing through charts, hospital records, and test results to get a full picture of each patient. Now, Navina’s AI copilot handles much of that data aggregation. The system pulls in information from across facilities (specialist visits, labs, imaging) and presents a concise summary for the provider. As a result, doctors are able to walk into appointments with all key facts at their fingertips, without tedious manual prep. Rockwood highlighted that this change has enabled clinicians to go home earlier and significantly lowered their stress levels. In other words, thoughtful AI integration both improves efficiency and helps tackle physician burnout.
For healthcare organizations, the takeaway is to insist on clinician-first AI, by involving end-users in the selection and configuration of AI solutions. Organizations should always measure usage rates: if doctors aren’t using the tool, they should find out the reason why and address it. Success in value-based care often hinges on clinician engagement; making AI adoption easy and beneficial for them helps lay the groundwork for sustained improvements in care quality and productivity.
Leverage AI for rapid quality improvements
AI’s capability to rapidly analyze and organize data has powerful implications for improving care quality metrics. In value-based contracts, performance on measures like preventive screenings, chronic disease control, and patient outcomes directly influences financial rewards. Traditionally, closing these care gaps and gathering data for reporting is labor-intensive. Staff might spend weeks combing through charts to find which patients need follow-ups or which metrics are falling short. AI can turbocharge this process.
Rockwood shared a compelling example: his organization used Navina’s AI solution to quickly identify and outreach to patients overdue for colorectal cancer screening. “What would have taken somebody 40-50 hours, to manually go in and abstract that patient information from records, took us an hour using AI,” he said. This late-in-the-year push, almost impossible without AI assistance, helped elevate their Medicare Star Rating on the colorectal cancer screening measure from 4.25 to a perfect five Stars.
This example underscores how AI can streamline quality improvement efforts. Some ways AI contributes include:
- Care gap identification: Scanning the EHR to list patients who haven’t received recommended services, such as vaccines, screenings, or check-ups, and might be overdue.
- Data aggregation for reporting: Pulling required data points for hundreds of patients across multiple systems, which ensures no missing information when submitting to payers or accrediting bodies.
- Prioritizing interventions: Highlighting which gaps are most critical (for example, patients at highest risk who haven’t had a follow-up) so teams can tackle the most impactful ones first.
- Monitoring performance in real-time: Dashboards powered by AI analytics can show up-to-date metrics across the population so that there are no surprises at year-end.
By leveraging these capabilities, healthcare organizations can shift from a retrospective, scramble-at-the-last-minute approach to quality metrics, toward a proactive and continuous improvement mode. It’s much easier to meet annual targets when you know by mid-year which areas are lagging. As Rockwood’s story illustrates, AI helps teams accomplish in hours what might otherwise take weeks, allowing them to capture full incentive payouts and, more importantly, close care gaps for patients faster. The actionable strategy here is to incorporate AI tools that align with key quality measures. For example, if diabetic eye exams are a priority metric, use AI to automatically scan for who hasn’t had an exam and generate alerts or outreach lists. Over time, this not only boosts scores and revenue, but also ensures patients receive recommended care, reflecting true quality improvement.
Foster transparency and embrace long-term value
A culture of transparency and continuous improvement can be a powerful driver in value-based care performance. Rockwood described how openly sharing performance data across clinician teams created friendly competition that lifted everyone’s results. “Just by nature, nobody wants to be the lowest on the totem pole,” he said. “You always want to make sure that you're above.” Jefferson City Medical Group found that greater transparency in metrics elevated the entire organization as best practices spread and individuals strove not to be the lowest performer.
For leadership, implementing transparency means developing internal dashboards or reports that show performance at the provider or team level, with appropriate context to keep it constructive. It’s important to introduce this carefully: Rockwood noted they gave underperforming clinicians a heads-up and support to improve. This way, transparency is used as a positive tool for improvement rather than a punitive measure. Over time, making outcomes visible to all staff encourages knowledge sharing: high performers can share what’s working for them, and lower performers can seek help and ultimately improve. The result is an organization that constantly learns and gets better, which is essential under value-based care’s demand for year-over-year improvement.
While fostering internal competition and improvement, healthcare organizations should also measure success with a long-term, holistic view because a short-term financial win means little if it causes long-term “abrasion” with clinicians or workflows. This is especially true when implementing innovative solutions such as AI. Meyers cautioned that return on investment in AI should account for less tangible–but no less crucial–benefits like improved coding accuracy, more timely interventions, reduced administrative burden, and higher clinician satisfaction. For instance, an AI tool that reduces physician burnout can prevent costly turnover–and ensure patients continue to receive high-quality care from experienced doctors. Likewise, better documentation and coding driven by AI can increase risk-adjusted revenue and prevent compliance issues, which may only show up in annual audits or future contract adjustments.
Organizations should track metrics like physician usage rates of AI, satisfaction surveys, coding error rates, and care quality improvements alongside the dollar figures. For example, if an AI system helped capture HCC codes appropriately, the immediate revenue might not be realized until next year’s settlement, but the organization should still consider this when calculating ROI. Similarly, time saved per clinician per day–and what they can do with that time, such as see an extra patient or finish work earlier–is a valuable return. Meyers highlighted that some initiatives can appear financially successful for one year but flame out in subsequent years if they were too hard on the providers. The goal is to achieve steady, sustainable gains.
Organizations interested in measuring long-term ROI should establish a broad set of KPIs for any AI or value-based initiative (cover patient outcomes, provider experience, and financial metrics), review them regularly–and be willing to iterate. They should communicate to stakeholders that success will be measured in multiple dimensions. Finally, by cultivating transparency in performance and taking a long-range perspective on value, healthcare organizations can ensure that improvements achieved with AI and other innovations are not only impactful but also enduring, ultimately securing their competitive edge in value-based care.
Conclusion
Value-based care requires healthcare organizations to excel on many fronts simultaneously– quality, patient satisfaction, cost management, and provider well-being. As these insights illustrate, AI can be a powerful ally in this journey, but only if implemented thoughtfully. Healthcare leaders should ensure they deeply understand their contracts, focus their efforts where they matter most, support and engage their staff, and deploy AI in a way that enhances–rather than hinders–clinical workflows. By proactively managing patient risk, targeting interventions to high-need groups, and leveraging data to drive quality improvements, organizations can significantly advance their value-based performance. Equally important is fostering a culture of openness and using comprehensive metrics to guide improvements for the long haul.
Navigating the complexities of value-based care is challenging, but these strategies provide a practical roadmap to delivering exceptional patient outcomes, achieving strong financial results, and sustaining those gains in the dynamic world of value-based care. By pairing value-based care principles with intelligent use of AI, organizations can secure a competitive edge and thrive in the new healthcare landscape.
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