In the past few years, enterprise adoption of technology and digitization has significantly increased, changing the landscape for healthcare providers and patients. To improve provider reimbursements, patient engagement, and care management and accelerate value-based care programs, healthcare enterprises have made digital adoption their growth strategies’ cornerstone.

To address these emerging enterprise demands, providers are expanding their AI offerings, engaging in vertical-specific partnerships and acquisitions, and investing in centers of excellence.

• Healthcare Provider Digital Services PEAK Matrix® Assessment 2024

  In this report, we examine 35 healthcare provider digital service providers featured on Everest Group’s PEAK Matrix® framework based on their capabilities. The report analyzes the provider digital services market size and growth and digital service themes for healthcare providers. It assesses providers on several capability- and market success-related dimensions and offers valuable insights to help enterprises make the right sourcing decisions.

  Scope

  • Industry: healthcare
  • Service: healthcare provider digital
  • Geography: global

  Contents

  In this report, we:

  • Assess healthcare provider digital services market trends
  • Examine each provider’s capability- and market success-related dimensions
  • Compare providers’ key strengths and limitations

  READ ON

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

The age of generic, one-size-fits-all healthcare solutions has ended. Patient and member engagement is vital to ensure personalized care for patients. Today, personalized communication is essential to enhance user satisfaction and ensure adherence to treatment plans. Personalized engagement in healthcare is moving beyond mere administrative support, focusing on enriching patient experiences, enabling proactive communication, and delivering timely interventions to foster better health outcomes.

• Patient and Member Engagement Platforms PEAK Matrix® Assessment 2024

  

  In this report, we assess 20 providers featured in the Patient and Member Engagement Platforms PEAK Matrix^® Assessment 2024. It will enable buyers to select the best-fit provider based on their sourcing considerations and empower providers to benchmark their performance against their competition.

  Scope

  • Industry: healthcare
  • Geography: global
  • This report is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with leading patient and member engagement platform providers, client reference checks, and an ongoing analysis of the patient and member engagement platform market

  Contents

  In this report, we assess:

  • Patient and member engagement platform market trends
  • Enterprises’ sourcing considerations
  • Providers’ key strengths and limitations

  READ ON

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

Healthcare-based enterprise priorities are shifting towards streamlining customer interactions, leading to higher demand and supply options for tech-enabled member and patient engagement solutions.

In this LinkedIn Live, our experts Priya Sahni, Chunky Satija, and Kaushik Sundar discussed investments across the value chain to better streamline and modernize the customer journey across multiple touchpoints. Our panel will explore key trends and drivers, as well as the intricacies of demand and supply in this emerging area of enterprise focus.

Viewers will come away with a solid understanding of the potential investments in this space, as well as best practices and key strategies for their implementation.

During this in-depth LinkedIn Live, we discussed:

• The key features that enterprises are looking for in member and patient engagement platforms when it comes to outsourcing
• The future of member and patient engagement
• The evolution of the supply ecosystem as it caters to enterprise demands

The healthcare industry is significantly investing in cloud services to implement use cases in health data analytics, value-based care, population health, and interoperability. As enterprises grow, they leverage industry cloud to future-proof technology infrastructure and enhance value delivery. In response, providers are forming industry-specific partnerships and investing in Centers of Excellence (CoE) and innovation labs to boost cloud capabilities.

• Healthcare Industry Cloud Services PEAK Matrix® Assessment 2024

  

  What is in this PEAK Matrix® Report

  In this report, we assess 34 healthcare industry cloud service providers featured on the Healthcare Industry Cloud Services PEAK Matrix® Assessment 2024. The report analyzes the healthcare industry cloud services market, cloud service themes for healthcare payers and providers, and providers’ capability- and market success-related dimensions. The research will enable enterprises to select the best-fit provider for their sourcing needs.

  Contents: 

  In this report, we:

  • Assess healthcare industry cloud services market trends
  • Examine each provider’s capability- and market success-related dimensions
  • Compare providers’ key strengths and limitations

  Scope:

  • Industry: healthcare All industries and geographies
  • Services: healthcare industry cloud
  • Geography: global

  READ ON

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

WATCH THE LINKEDIN LIVE ON-DEMAND

Recent interest in leveraging technology for the management of care has driven the healthcare industry to invest in digital technology that enables remote and efficient care. Though a large focus is on lowering costs and improving health outcomes via technology, there is also an interest in using technology to deliver care in-home, with clinical decision-making assistance and greater coordination as a feature.

Watch on demand this LinkedIn Live conversation with Everest Group analysts to learn about the evolution of technology-based care management solutions and how they will impact the future of the healthcare industry.

During this LinkedIn Live, we’ll discussed:

Which areas are of interest to enterprises digitizing care?
How is technology being leveraged to reshape the delivery of care?
How are solutions providers and platform providers enhancing their care management offerings?

Healthcare enterprises have made digital adoption the bedrock of their growth strategies to optimize processes, realize cost efficiencies, and enhance member experiences. They are leveraging digital services for value-based care and population health management and to comply with changing regulations. To support enterprises on their digital transformation journeys, providers are ramping up their capabilities through vertical-specific partnerships and acquisitions, investments in Centers of Excellence (CoEs), and training, among other things. These efforts are, in turn, driving the need for research and market intelligence on demand and supply trends in healthcare payer digital services.

• Healthcare Payer Digital Services PEAK Matrix® Assessment 2023

  

  What is in this PEAK Matrix® Report

  In this report, we assess 32 healthcare ITS providers, which are mapped on the Everest Group PEAK Matrix®, a composite index of distinct metrics related to a provider’s capability and market impact. We focus on payer digital services market size and growth, digital service themes for healthcare payers, provider assessments on several capability- and market-success-related dimensions, and Everest Group’s independent assessment of the providers to help enterprises make the right sourcing decisions.

  Content:

  This report comprises three sections:

  • Healthcare payer digital services market trends
  • An assessment of 32 healthcare payer digital service providers on capability- and market success-related dimensions
  • Enterprise sourcing considerations, highlighting the strengths and limitations of each healthcare payer digital service provider

  Scope:

  • Industry: healthcare
  • Offering: payer digital services
  • Geography: global (with focus on the US)

  READ ON

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

The healthcare sector is increasingly interested in harnessing generative AI capabilities to drive operational and cost efficiencies. Leading technology players are heavily investing in this space and partnering with healthcare enterprises to take advantage of its potential. Discover the latest developments, challenges, and outlook for generative AI in healthcare in this blog.  

The adoption of generative artificial Intelligence (GAI) by the healthcare industry has gained momentum in recent months with technological advances such as ChatGPT and Dall-E 2.

To tap into this growing demand for generative AI in healthcare, BigTechs (Amazon, Google, Microsoft, and Oracle) are leaving no stone unturned as they pivot toward developing capabilities at the intersection of healthcare and GAI. Let’s explore this further.

Recent healthcare-specific GAI announcements by BigTechs

The frequency of recent investments by these tech giants signals that more funding will follow in the GAI-healthcare space.

BigTechs’ partner ecosystem – a key differentiator in developing industry-specific generative AI capabilities

As BigTechs make huge strides in ramping up their healthcare-specific generative AI capabilities, accessing large volumes of healthcare data is a major obstacle. To address this challenge, BigTechs are forging strategic partnerships with niche health tech firms or third-party data providers to obtain healthcare data for training Large Language Models (LLMs).

BigTechs also are leveraging partnerships to foster co-innovation and adopt a joint go-to-market strategy for healthcare-specific GAI solutions. For example, Oracle partnered with AI specialist firm Cohere to develop GAI solutions. Through the partnership, Cohere will train, build, and deploy its generative AI models on Oracle’s cloud infrastructure. Oracle plans to deploy new models for healthcare and embed GAI throughout its industry-specific applications.

BigTechs’ GAI capabilities are predominantly provider-focused  

Most investments by BigTechs in generative AI in healthcare are focused specifically on enhancing administrative processes for providers and physicians. Some of the prominent use cases are centered around streamlining clinical documentation and drafting automatic message responses to patients, as illustrated below:

Seeing how the technology giants develop capabilities in care coordination and delivery will be interesting. Providing health and lifestyle recommendations, reminders for medications and appointments, and other proactive communications could significantly transform the way patient care is delivered.

When developing care coordination and delivery capabilities, BigTechs and other technology players must ensure the GAI models are built on huge volumes of patient data. This will significantly reduce the margin for error and help improve patient care outcomes.

Adoption of generative AI in healthcare is currently limited but will surge before long

While investments by suppliers are rising, GAI adoption in healthcare is still in its nascent stages. However, we have observed a few mid-to-large-sized hospitals and providers taking the leap of faith and becoming early adopters. For example, UC San Diego Health and UW Health in Madison, Wisconsin, are adopting GAI solutions developed by Microsoft and Epic to automatically draft message responses to patient queries.

To increase GAI adoption in healthcare, technology players and enterprises will have to jointly address these key considerations:

• People – determining and acquiring the right skills
• Process – change management and identifying the business functions
• Technology – ensuring security and maintaining infrastructure

Most importantly, successfully navigating security challenges will be key to increasing adoption, as enterprises are skeptical about sharing sensitive patient data to train GAI models. Technology players will have to proactively alleviate this concern by ensuring patient data is protected and that the development of GAI models adheres to data privacy and state and federal government security mandates.

Moving forward, what will BigTechs focus on?

While most current use cases revolve around administrative and operational provider functions such as clinical documentation and medical coding, BigTechs are expected to invest in other provider-related functions shortly. Some provider-specific functions that are ripe for GAI investments are:

• Billing and payments – Analyzing patient billing data, payment history, and due dates to generate personalized payment reminders for patients
• Post-discharge follow-ups – Generating personalized care instructions and sending tailored health reminders to individuals, including medication adherence and lifestyle recommendations

Pre-care and post-care services, such as providing appointment reminders and billing options, could be near-term investment areas for BigTechs. By working together, BigTechs and enterprises can potentially leverage GAI to analyze patient data and proactively recommend care interventions – an exciting concept to keep an eye on.

Surprisingly, we have not seen much traction or activity in the payer segment on the supply or demand side. Considering payers are generally more inclined to embrace technology than providers, we anticipate demand and supply to grow in the short term.

It will be intriguing to watch the types of applications BigTechs create for payers when they dive deeply into this segment. Streamlining contact centers and automating benefit verification are low-hanging fruits BigTechs may target.

We will continue to closely follow how BigTechs’ GAI play evolves in the payer market. To discuss generative AI in healthcare, contact kaushik.s@everestgrp.com and priya.sahni@everestgrp.com.

Learn more about technology in healthcare in our LinkedIn Live session, How Technology is Reshaping the Delivery of Care in the Healthcare Industry.

Generative AI (GAI) has disrupted numerous industries, and the healthcare industry is eager to join in and explore the applications of GAI in healthcare research. However, the healthcare sector must be cautious due to the potential risks. Read on to learn more about Generative AI in healthcare, including adoption, usage, and risks.

Reach out to us directly for questions or further details.

Generative AI, an advanced technology that employs deep learning models, can create images, videos, text, codes, simulations, and other high-quality content by responding to given prompts within seconds.

While GAI has been shaking up almost every industry with its easy-to-use interface and instant responses, the healthcare sector is typically slower to adopt new technology, and the risks of inappropriately deploying the technology are huge.

Nevertheless, technology giants and healthcare startups are racing to test the potential for large language models (LLMs) and GAI tools in healthcare research. Understanding the adoption, usage, and potential risks of GAI in the healthcare setting is crucial.

With its vast applications, it is important to carefully select the use cases that can positively impact patients with minimal regulatory, compliance, cost, and health risks. Let’s explore this further.

By using the below framework, healthcare organizations can prioritize use cases for observation, exploration, and future investment:

Based on our analysis, GAI offers the greatest immediate potential in the area of clinical documentation. The technology can be used to free scarce clinical resources from time-consuming administrative tasks, allowing them to instead focus on delivering quality care to patients. For example, simplifying clinical documents and creating easy-to-review clinical patient summaries are some areas where GAI can have the greatest short-term impact in healthcare.

Looking beyond these basic applications, healthcare stakeholders need to cautiously consider using GAI for diagnosing patients or directly providing medical care. The technology’s tendency to sometimes invent a response when it lacks sufficient information makes it too risky for care delivery. Significant long-term investments will be needed before GAI can be used for delivering patient care.

Will Generative AI in healthcare lead to job loss?

GAI is expected to considerably alleviate the administrative burden of healthcare professionals. The nature of jobs will shift and healthcare professionals will have to adapt as demand grows for GAI. Certain healthcare professions such as medical transcriptionists, medical record keepers, medical coders, call center executives, and home healthcare executives will need to upskill as GAI automates some manual processes.

The other big question in everyone’s mind is whether GAI will completely replace physicians. This is unlikely to happen anytime soon. GAI should be viewed as a tool to augment healthcare professionals’ capabilities and not replace them entirely. Physicians, surgeons, radiologists, and nutritionists will leverage GAI to enhance care delivery.

Challenges to future adoption

Generative AI in healthcare is a disruptive opportunity that both excites and concerns healthcare professionals. While it offers significant potential, the industry needs to overcome obstacles to commercial adoption.

Some of the risks include:

• Ethical concerns and biases – Healthcare professionals must be wary of the ethical concerns and limitations related to using sensitive member/patient information. Moreover, limitations of datasets also can lead to biases in results or outcomes suggested by GAI
• Training data limitations – Generative AI models require high-quality training data to improve the accuracy of output, which can be difficult to source. The model’s efficiency also depends on the breadth of training data, which can be both time-consuming and expensive to label
• Integration issues – Integrating GAI into existing healthcare systems and workflows can be challenging, especially with legacy systems that are not designed to work with AI
• High costs – The technology costs of building and deploying GAI models can be expensive

Adopting Generative AI in healthcare can be a game changer. While it offers many potential benefits, the industry needs to fully understand the associated risks of GAI before implementing use cases and adopting the technology at scale.

To discuss the future of Generative AI in healthcare, contact Kaushik Sundar, kaushik.s@everestgrp.com, or Priya Sahni, priya.sahni@everestgrp.com.

Continue learning about the healthcare industry in our webinar, How Technology Can Help Healthcare Overcome the $30 Billion RCM Spend.

The CMS Interoperability and Patient Access final rule has enabled key healthcare stakeholders – payers, providers, and health IT vendors – to realign their strategic goals and work toward enhancing member engagement and care delivery.

While interoperability in healthcare can deliver numerous benefits, complying with the rules can be complex and we are closely tracking this issue. In our earlier blog, we covered the evolution of interoperability over the years, the interoperability rule, and the challenges enterprises face in deciphering this regulation.

Read on for part two in our blog series that focuses on the data sets that need to be shared, steps involved in the data sourcing process, and the areas enterprises must focus on to navigate through the interoperability rule.

Which data gets shared as part of the interoperability rule, and what is the data sourcing process?

The interoperability rule has mandated payers to share across member- and plan-level information with the help of two Application Programming Interfaces (APIs) – patient access and provider directory. The rule also clearly identifies distinct data sets that need to be shared through both the APIs, as illustrated below.

Having discovered what data needs to be shared, the next big question for enterprises is understanding how to extract this data. To make the necessary data available to its members through open APIs, enterprises primarily have to perform these three key steps: source system identification, data mapping, and data transformation.

• Source system identification: As healthcare organizations store member information across multiple systems such as claims management system, Electronic Health Records (EHRs), etc., the primary objective is to identify the right source systems that house the information needed to be shared through the APIs
• Data mapping: Data elements mandated by CMS are populated across various Fast Healthcare Interoperability Resources (FHIR) profiles such as patient profile, practitioner profile, etc. These data elements must be mapped against the respective source systems by matching the fields from the source database to the target database
• Data transformation: FHIR profiles consist of data elements with attributes such as cardinality, data type, and binding value sets. The mapped data will have to be transformed into the FHIR recommended format by adhering to the data attributes (for example, translation of system codes into industry-specific codes, usage of industry- standard unique identifiers such as National Provider Identifier (NPI), Clinical Laboratory Improvement Amendments (CLIA) number, etc.)

 How do enterprises navigate through the CMS interoperability rule?

Although the interoperability rule defines IT investments payers, providers, and Health Information Technology (HIT) vendors must make, enterprises also need to plan for other critical aspects such as infrastructure scalability and data security in parallel. These areas will be crucial given the increasing data volume and demand for more streamlined services around data access and utilization.

The exhibit below illustrates the key IT remodeling themes and corresponding transformation levers for interoperability implementation in a healthcare enterprise.

FHIR-based API ecosystem

The interoperability rule states that healthcare enterprises should establish API interfaces for all systems handling member/patient data and that the data transferred among healthcare entities – including the member/patient – should be in a standardized format. A robust API-led interoperability strategy can help healthcare enterprises curb the data liquidity issue within their ecosystems. The FHIR-based APIs will enable data format standardization between different endpoints, decrease development time, and save storage space on endpoint devices.

But just creating and establishing FHIR-based APIs will not suffice. Enterprises need to integrate and orchestrate formats other than FHIR. While connectivity with standard or off-the-shelf systems will be easier, homegrown/custom systems will be challenging to map to FHIR standards. In-house development teams and technology vendors will have to create workarounds to modify existing components that consider the potential variability in medical terminologies.

Infrastructure layer

With the implementation of FHIR-based APIs, enterprises must assess scalability challenges within their existing infrastructures. To accommodate the upcoming member/patient data access requests and enable quick data retrieval, enterprises should start to manage their current data storage and compute capacities. Enterprises can approach the data scalability and infrastructure issue by either leveraging existing infrastructure to build an FHIR-based layer or partner with technology vendors to leverage their data, cloud, or FHIR platforms.

Security layer

As healthcare enterprises will have access to multiple data sources, healthcare interoperability might open the door to security breaches and cybersecurity threats that may not have existed if the data resided within the enterprise. With the influx of data from other healthcare entities, current standard security checks might not be able to cross-reference and validate the identity of the entity requesting access, creating openings for data breaches. To manage these security challenges, added investment in particular focus areas (e.g., application penetration testing, consent management, member education) can help enterprises achieve sustainable data security.

The road ahead

While enterprises are complying with the CMS mandate, an increased focus must be put on how they can look beyond regulations to address some of the key pain points in the industry, such as patient experience, care management and outcomes, and total cost of care. With data flowing seamlessly across the healthcare ecosystem, enterprises should identify and invest in areas that would be crucial to creating long-term business value while also giving them a competitive edge.

As part of our third blog in this series, we will next cover how healthcare enterprises can approach the interoperability rule beyond the mandate to reap long-term benefits, key investment areas, value for enterprises, and an interoperability enablement framework that provides a view into the required IT components for regulatory compliance and what goes beyond regulation.

Please feel free to reach out to Kaushik.S@everestgrp.com to share your experience and ask questions.