2022 Analytics for Value-Based Care

Despite slow macroeconomic shift, VBC solutions continue advance to meet evolving healthcare needs

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This report is also available to members of the Chilmark Advisory Service through the portal.

The shift to value-based care (VBC) has long been an industry talking point in healthcare, among vendors, providers, and patients alike. Perhaps predictably, for a sea change of this magnitude, migration to these new reimbursement models has been slow. Notably, COVID-19 hammering hospital systems’ typical fee-for-service business model left little wiggle room to implement a new pricing structure like the VBC model, and some systems that had planned for the shift were forced to backtrack.

As value-based care becomes increasingly seen by policy makers as the most viable option to “bend the cost curve,” policy and incentives are aligning for the adoption of value-based care. IT-based solutions are becoming more robust, while legislation like TEFCA and the 21st Century CURES Act are cementing the direction forward.

So, what would it take for a hospital system to successfully drive this shift to VBC? What solutions can health IT vendors bring to market that facilitate this transformation? In Chilmark Research’s latest report, “Analytics for Value-Based Care”, we examine the leading vendors who are tackling this problem with analytics solutions that are foundational to success in any VBC program.

The Center for Medicare and Medicaid Services (CMS) has been leading the migration to VBC through a number of programs, many of which now have downside risk. Most notably are the Medicare Shared Savings Plan (MSSP), an accountable care organization (ACO) model, and Medicare Advantage. The new REACH ACO model goes even further towards capitation.

Medicaid, administered at the state level, is also increasingly moving to VBC models of reimbursement. Today, several states have migrated their Medicaid payment structure to a full capitation model, wherein provider organizations are paid a fixed, negotiated fee per patient per month.

Commercial payers are also following the government’s lead, most notably through Medicare Advantage, and some large, self-insured employers are pursuing VBC via direct contracting with health systems.

There are three main functional analytic categories for VBC: network design and optimization, facilitating care management (includes population risk assessment), and contract negotiation/management. In “Analytics for Value-Based Care”, we examine, evaluate and profile 17 leading vendors. Our analysis combines primary and secondary research, including in-depth demos with each vendor, to assess the relative strength of their solutions to facilitate a health systems migration to VBC.

This research report will assist all healthcare stakeholders in understanding what is available in the market today and the relative strengths of each vendor in helping provider organizations migrate to VBC. No other report provides this level of depth and breadth to assist organizations in becoming a data-driven enterprise and being successful in their future journey to VBC. For vendors, providers, investors, and more, this report is releasing on October 17 and is available for purchase or as part of a subscription to the Chilmark Advisory Service. Contact colin at chilmarkresearch dot com for any inquiries.

Vendors Profiled: Arcadia, athenahealth, Cedar Gate Technologies, Oracle Cerner, Change Healthcare (Now part of Optum), Clarify Health, Health Catalyst, HealthEC, Innovaccer, Lightbeam Health Solutions, Medecision, Merative, Milliman, NextGen, Optum, Persivia, SPH Analytics (Now part of Press Ganey)

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2022 AI and Trust in Healthcare Report

A how-to guide for protecting and cultivating trust in the early days of applied big data

This report is available to subscribers of the Chilmark Advisory Service via the client portal, or may be purchased separately by clicking below.



Learn how to think about developing and implementing new “artificial intelligence” tools to aid in healthcare delivery without compromising brand integrity and the faith of the communities you serve. An in-depth guide on what trust means and how to cultivate it as we sit at the precipice of broadly-available, algorithmically-assisted decision making in healthcare.

Coverage of this report was one of Healthcare IT News’ Top 10 AI and Machine Learning Stories of 2022.

About the Report

Trust is becoming a form of social capital in the healthcare AI arena that demands a comprehensive approach.  A number of flawed algorithms have entered the market and been found to include bias and lack reproducibility or transparency. This has the impact of damaging trust; more needs to be done to foster safe and validated algorithms that can improve outcomes, health equity and clinical work burdens.

Tools and processes have been developed to address bias and these need to be supported through building diverse data science teams.  A number of technological tools and checklists have been developed to address racial and gender bias in algorithms. These tools can be adapted to healthcare and built upon.

More cooperation across the industry is needed to create processes for Good Algorithmic Practices across use cases and the lifecycle of algorithms. The FDA has fallen behind and does not address the entire spectrum of algorithms. Industry consortia are urgently needed to act as a “Consumer Reports” on algorithms and create certification processes across the various stages of the lifecycle.

AI and Trust in Healthcare examines the growing role of AI in healthcare and the underlying factors that can both harm and help build trust by (and for) end-users of products and services that utilize artificial intelligence. The report also proposes an intra-industry consortium to address some of the critical areas that are central to patient safety and building an ecosystem of validated, transparent and health equity-oriented models with the potential for beneficial social impact.

Over the past several years, AI has become one of the most discussed technologies in society. With the potential to determine who gets what form of medical care and when, the stakes are high with AI algorithms if they are not deployed with care. Already we have seen many examples of algorithms containing bias with respect to race and gender enter the market, and there are many clinical decision support tools being used that still have problematic science behind them.

There are eight components of responsible AI: Data Governance, Validation & Reproducibility, Human-Centric, Accountable, Transparency & Explainability, Safe & Reliable, Audited for Bias, and Fairness & Health Equity

The eight components of responsible AI (as of 2022)

A review of clinical algorithms currently in use across multiple specialties found a rather large number of cases where race correction was used inappropriately. Earlier this year we discussed additional cases in our podcast episode with Dr. Tania Martin-Mercado, who highlighted the case of the algorithm used for kidney disease, the glomerular function algorithm, which results in African-Americans waiting longer for kidney transplants.

During the first year of the COVID-19 pandemic, hundreds of algorithms were developed to aid in diagnosis through analyses of x-rays and CT scans. One study showed that none of these algorithms were reproducible. The reproducibility crisis in AI in medicine has the potential to undermine trust in AI products by both providers and patients. Princeton University researchers have recently held a workshop and released a white paper on the extent of this problem in machine learning including many examples in medicine.

The “AI and Trust in Healthcare” report provides an overview of some of the challenges in building AI models for healthcare and medicine, the tools and processes that can be used to address problems such as bias and drift and the steps companies can take to build trust by following both good data science and intentional efforts to build diverse teams capable of addressing the multiple axes of bias.

Finally, the solution to these problems requires more than the attention of individual companies. The FDA and regulatory environment have fallen behind in addressing the challenges that confront a rapidly growing technology with high stakes. The author proposes consortia around the various use cases for AI that would provide a more transparent and scientifically rigorous approach to certifying algorithms, after they are assessed for validation, data governance, bias, explainability and impact on health equity.

In addition to the consortia for AI in healthcare we examine a recent proposal that calls for the use of liability insurance in AI for healthcare to drive adoption of the highest quality algorithms. The certification process that AI consortia would develop could work in tandem with the insurance industry to certify vetted algorithms that would receive lower premiums for going through the certification process.

Readers of our report will learn about the state-of-the-art processes for bias and risk mitigation that draws upon work developed within government and think tanks with programs focused on bias and AI. We link these processes to some emerging data science work on the complexity of digital health data. This will be of use to both data scientists and executives interested in developing innovative machine learning tools that have a reduced risk of doing harm.  We welcome feedback on the report and Chilmark Research will be working to catalyze the consortia for AI as well. Please feel free to reach out to the author, Dr. Jody Ranck at jody at chilmarkresearch.com.

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2022 Technologies Driving Improved Healthcare Experiences

This report is available to subscribers of the Chilmark Advisory Service via the client portal, or may be purchased separately by clicking below.

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As options for care delivery grow, so too do consumer expectations. Providers are looking to leverage new technologies and platforms, both to streamline the patient journey through a healthcare system, as well as ease the burden on their own resources.

What does the future hold for healthcare institutions and consumers as both patient expectations and provider capabilities grow and change? How have expectations changed following the adoption of new tools and services during the pandemic?

In this report, we’ll be looking at patient-centric solutions and their futures. These will be:

  • Offerings that at least partially allow the patient to engage with the provider to resolve direct healthcare needs;
  • On-demand, or not limited by appointments, hours of operation, or similar considerations;
  • Part of a closed workflow loop, not intermediary solutions – designed to be part of an integrated medical experience and not simply a component of that experience (such as a basic payment or scheduling app). 

This research will define the scope of capabilities an ideal experience strategy will enable to meet current market demands, with an eye towards ongoing development and future needs. The report will explore the current market dynamics driving adoption, key vendors operating in the space with evaluations of their solutions, and emerging ‘vendors to watch’ taking notable approaches to solving a problem in this niche.

In a rapidly shifting landscape, we can help you filter the the signal from the noise. With all the hype around consumer-driven healthcare, rely on our analysts to provide pragmatic insights on the key trends relevant to your organization’s strategy.

This report features the following vendors:

Cost: $6,000

This report is available to subscribers of the Chilmark Advisory Service via the client portal, or may be purchased separately by clicking below. Check out the podcast Sr. Analyst Jody Ranck did with Beckers Healthcare on this topic – click here.

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Many health systems and hospitals suffered a severe financial shock with the onset of the COVID-19 pandemic in March 2020. With hospital operating margins already slim, such a shock can drive margins into negative territory and impact the survival of the organization. Because of this, the pandemic created an opportunity for a number of AI in Operations (AI4Ops) vendors to demonstrate a measurable ROI during the crisis, and emerge stronger for it.

While most of the attention in the AI space has focused on clinical applications, AI for operations – AI4Ops – is where the real action is today. Smart money is catching on to the potential market for reducing overhead and administrative waste in healthcare, with recent investments and M&A activity in this space reflecting that.

This report takes a closer look at several leading solutions in the AI4Ops space and provides an overview of the trends and dynamics of the market as it evolves post-pandemic:

The research focused on the largest segments of the operations spectrum from supply chains to hospital operations. We have also included a small number of supply chain and RPA vendors that we believe compose an important, but smaller segment than RCM and hospital operations.

Evolution in Ops Approaches

Evolution of Healthcare Operations Approaches

The report covers a number of operational use cases including the following:

  • Discharge Planning: ER management, Discharge barriers, Perioperative
  • Hospital Operations: Staffing, Hospital beds, Surgery, Asset optimization
  • Revenue Cycle Management (RCM): Intelligent claims management/denial prevention, Prior authorizations/eligibility, Patient ability to pay, Fraud detection
  • Supply Chains: Predictive caseloads, Supply chain forecasting, Resource prioritization

Many healthcare organizations have been using point solutions for operations as they initially engage with AI, but the leading vendors are building multi-functional, end-to-end platforms across operations and RCM functional areas.

The report provides clear examples of how healthcare leaders can learn from other industries such as airlines and airport capacity management to improve their own operations. We also highlight vendors who excel at the change management component of AI4Ops, which is mission-critical to successful digital transformation.

As an essential piece of digital transformation, AI4Ops will continue to relieve the burden on an already stressed healthcare system, and should prove a testing grounds for developing trust in AI offerings within the healthcare industry.

Healthcare administrators and leaders looking for ways to improve their operations processes will find this report useful in assessing which vendor and solution is right for their organization’s needs. Operations executives can expect to find clear breakdowns on AI offerings that will alleviate many of the stresses facing their organizations today. Investors, solutions vendors will find value in the outlines of market trends as well as the competitive landscape, and the market sizing projections will assist in targeting customers for their products.

Vendors Profiled: Change Healthcare, Codoxo, Health Catalyst, HospitalIQ, Infinitus, LeanTaaS, Olive, Optum, Qventus, Waystar

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The Health Cloud Data Analytics Hub

The U.S. healthcare industry is undergoing tectonic change. Market and economic forces, as well as the legacy of the industry’s past business and technical practices, are causing every healthcare enterprise to look for untapped opportunities to realize a greater return on data and data infrastructure investments.

The historic shift from a fee-for-service-based (FFS) revenue model to value-based healthcare is causing an industry-wide reevaluation of the effectiveness of clinical, business, and financial processes. This transition will allow the industry to serve patients and members more equitably with high quality, cost-effective healthcare services. Healthcare enterprises of all kinds have turned to cloud computing to better leverage their data assets to drive new innovations in analytics and reporting.

To capitalize on this innovation opportunity, healthcare enterprises must partner with vendors who can deliver not only cloud-native technology, but also bring a strong understanding of the unique challenges presented by healthcare data. Healthcare data is highly variable in its formats, expressions, and degree of standardization. The industry’s own standards are not always followed; local variants often become the norm inside many organizations. Healthcare data types are proliferating and the data itself is becoming more widely available for uses across the healthcare system.

Healthcare is also massively siloed, which has led to enormous data redundancy inside and across different enterprises. Healthcare enterprises must be able to cope with these challenges to effectively source, prepare, and stage this data for healthcare analytics.

This research brief introduces the idea of a Health Cloud Data Analytics Hub. It shows how the combination of cloud computing and advanced tools for healthcare data management can help healthcare enterprises enable both users and developers to do more with data. It describes how cloud and other technology vendors can support organizations with varied skills and people resources available to work on analytics efforts. It will also help set some reasonable expectations for selecting and working with such vendors.

Disclosure: This research brief was produced with support from Actian Corp given our shared interests in educating users about the importance of effective cloud infrastructure in healthcare, which we have been writing about for years now. You can be confident that even when producing sponsored content, our objectivity in presenting the materials is fundamental to our credibility and mission, so these are developed to be educational, never promotional.

Patient-Centric Communication for the Omnichannel Future

Patient Relationship Management: Understanding the full context of the patient in an increasingly complex healthcare system 

CRM and the platforms which enable it were pioneered by the hotel and hospitality industries, not just to bring in new customers, but also to build and maintain their relationships with existing customers. Recognizing that a loyal lifetime customer is more valuable than a new one, their goal was to breed loyalty through communication and familiarity. A CRM manages outreach, PR, and loyalty programs. It records a customer’s history not just at a single location but throughout an entire chain, noting their preferences, details of prior stays, what promotions and enticements have been most successful to bring them back, and the entire network of relationships that can exist between a commercial organization and a potential customer. 

The Patient Relationship Management platform puts clinically relevant, non-encounter-based interactions at the forefront of its workflows. PRM platforms understand how vital good communication and outreach are beyond just producing encounters or influencing patient experience and satisfaction metrics. Through improved communication, they help build vital relationships and drive important clinical outcomes. The core of the PRM is the contextual use of information. Leveraging data and maintaining a fuller, more complete view of the patient context, PRMs enable care activities, messaging, and the delivery of the content which is most important to patients and their health. By going beyond data within the EHR and looking at the full breadth of sources of clinical care a patient may receive, they offer a fuller view of patient needs and a better understanding of communication the patient is already receiving. 

This research brief provides ideas and insights for healthcare organizations seeking to understand the differences between Consumer Relationship Management and Patient Relationship Management. It can assist with the identification of organizational needs and digital transformation efforts. As organizations look to select and work with relationship management vendors, they can apply what they learn here when defining reasonable expectations, identifying necessary components, and selecting the correct platform for their goals.

From Connectivity to Real Provider Usability: Enhanced Reconciliation for Healthcare Data

This research brief describes an approach to enhanced data curation (de-duplication and reconciliation) that will support the integration of any outside data sources and data classes into a patient’s chart. It meets the organization’s need for more satisfied, efficient clinical users by satisfying clinician’s need for complete information and simplified workflows.

The Health Cloud: Key Infrastructure for Digital Transformation

This report is available to subscribers of the Chilmark Advisory Service via the client portal, or may be purchased separately by clicking below. To accompany the release of this report, author Alex Lennox-Miller hosted a live roundtable in June with Dr. Don Rucker, Julia Millard (Bright.MD) and Mike McSherry (Xealth), which can be viewed here.

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Virtual care plays an essential role in solving the problems of modern healthcare. The necessary remote appointments and home care of the COVID pandemic have driven use of virtual care to new heights, but its real promise goes far beyond these uses. When seen as part of a system of distributed healthcare, virtual care can play a vital role in the long-term care relationships that occupy most of the modern healthcare system.

This research report looks at the rapidly growing market opportunity, which at a CAGR of >20% is projected to reach in excess of $20 billion USD by 2027. The report segments growth in the four leading markets for VCM: commercial payers, self-insured employers, health systems, and independent ambulatory practices.

The work looks closely at a broad cross-section of vendors that are leading the VCM charge, ranging from incumbent EHR and population health vendors to best-of breed solution providers. The research also discusses the most significant trends in technology, reimbursement and regulation, and how those will impact adoption and implementation of these offerings over the coming years.

Each type of solution (EHR, PHM, Best of Breed) is evaluated based on how they address the needs of providers and patients. The report reviews the current state of the market, the maturity of solutions, and the strengths and weaknesses of each solution type. Each vendor profiled is evaluated on 17 metrics – twelve (12) for specific product functionality and five (5) for market execution.

This report also introduces the new Flagship Vendor category, which recognizes solutions that excel in each of the aggregated capability categories. For this report, those vendors are (Vendor – Category, presented alphabetically):

  • Amwell – Omnichannel Functionality
  • Innovaccer – Enrollment and Assignment
  • Epic – Reporting and Analytics
  • Neuroflow – Care Management
  • Teladoc – Patient Engagement

Vendors Profiled: Amwell, athenahealth, Bright.md, Caregility, Cerner, Doxy.me, Epic, Gyant, Health Catalyst, Innovaccer, NeuroFlow, NextGen, Persivia, Philips, Silvercloud, SymphonyRM, Teladoc/Livongo

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Chilmark Research’s latest report, Integration Infrastructure: Building 21st Century Healthcare IT, captures a market transitioning to new approaches to development and integration, enabling greater usability of data across organizations and applications. Pressure has long been building towards streamlining the flow of data between provider, payer, and patient. Driven by both market and regulatory forces, the movement towards better data availability will increasingly rely on APIs and other services to provide this connectivity.

Data has long been a treasured commodity in the healthcare industry, and many enterprises are seeking faster, cheaper routes to utilization as the quantity and liquidity of data increases.

This research predicts the market for integration technology and services will amount to $2.09 billion by 2026, representing 14% CAGR across all healthcare sectors. However, non-traditional sectors, including Life Sciences, will grow far faster (3-4 times the rate in traditional buyers) and will amount to the largest single market for this technology by 2026. 

Data aggregation, until now mostly a precursor for analytics and reporting applications, is important for a more diverse set of applications and range of access requirements. Building the data stores behind APIs, creating cohort-level data on demand, and supporting analytics on-demand are examples of growing market needs that this report discusses.

The building of new price transparency tools for members and patients will be greatly aided by the products and services described in this report. Many providers and payers will use the data aggregation capabilities highlighted to harness new data, contributing to a better understanding of their pricing position compared to similar local organizations.

Quality of care, growth potential, and regulatory compliance all hang in the balance as the race to greater data liquidity continues. Purchase a license to this report today help you understand how each vendor differentiates and what to consider when planning your own development and integration strategy.

Vendors Profiled: Allscripts, Amazon Web Services (AWS), CareEvolution, Cerner, Epic, Google Cloud, Health Catalyst, InterSystems, Lyniate, Microsoft, NextGen, Orion Health, Philips, Redox

Cost of report: $6,000

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