What You Should Know:

–       Accenture Federal Services has entered into an agreement to acquire Cognosante, a mission-driven provider of digital transformation and cloud modernization solutions for federal health, defense, intelligence, and civilian agencies. 

–       Founded in 2008 by Michele Kang, Cognosante has grown rapidly to become a trusted provider of innovative technology solutions with its federal government clients, including healthcare programs supporting veterans, active-duty military, patients, beneficiaries, providers, and payors. 

Accenture Federal Services Welcomes Cognosante Team: Driving Innovation and Impact for the Nation

The company boasts an outstanding record of delivery, driven by expertise in technology, capabilities in cloud modernization and migration, and exceptional program management. It is renowned for its commitment to providing excellent user experiences, enhancing accessibility and equity in healthcare, and optimizing the value of investments by the federal government.

Cognosante, with a team exceeding 1,500 individuals, will merge with Accenture Federal Services, which comprises over 14,000 members. Together, they aim to redefine work processes, tackle critical challenges, and instigate meaningful transformations. The finalization of the acquisition is contingent upon regulatory scrutiny and other customary closing requirements, with the transaction terms remaining undisclosed.

As a prominent US federal services firm and a subsidiary of Accenture LLP, Accenture Federal Services empowers the federal government to surmount obstacles, achieve superior outcomes, and establish a digitally agile, intelligent, and secure core. With a workforce exceeding 14,000, they are dedicated to advancing the vital priorities of their clients, enhancing national strength and security, and improving quality of life for citizens. Leveraging Accenture’s global network across diverse industries, they introduce commercial innovation to solutions crafted through advanced research and development, emerging technologies, and human-centered design, executed with rapidity and scalability. Together, they aid clients in generating enduring value for their personnel, clientele, and collaborators, thereby effecting positive change for the nation and its communities.

“We are continually innovating and investing to help federal agencies stay ahead of the ever-changing needs of their mission and customers,” said Accenture Federal Services CEO John Goodman. “Accenture Federal Services is excited to welcome the Cognosante team. We are truly impressed with their people, capabilities, and impact. Together, we will deliver on the promise of technology for the nation’s priorities.” 

What You Should Know: 

– Accenture and Salesforce are investing in the development of Salesforce Life Sciences Cloud, focusing on innovation, assets, and accelerators powered by data and artificial intelligence (AI) to help life sciences companies create sustainable value and drive growth.

– Building on their generative AI collaboration, Accenture and Salesforce will leverage their joint generative AI acceleration hub to develop new solutions and use cases for Salesforce Life Sciences Cloud.

Salesforce Data Cloud and Einstein AI

According to Accenture Research, 58% of life sciences companies consider the accelerated adoption of data and analytics capabilities as a top investment priority. As part of the collaboration, both will use Salesforce Data Cloud and Einstein AI, Salesforce’s AI technology, to enhance productivity and transform healthcare professional and patient experiences. 

The patient services Health Cloud assessment framework generates insights and a value case for Salesforce Life Sciences Cloud. The pharmaceutical Data Cloud and Einstein AI accelerator leverages generative AI to provide a holistic view of provider and patient data and suggest the most important messages for patient interactions.

Future Development

Accenture will continue to develop complementary tools and methodologies to help clients test and pilot the new capabilities of Salesforce Life Sciences Cloud.

What You Should Know: 

– Accenture announced it has acquired Nautilus Consulting, a digital healthcare consultant firm in the U.K. that specializes in Electronic Patient Record (EPR) solutions. Financial terms of the acquisition were not disclosed. 

– The acquisition will enhance Accenture’s digital transformation, implementation, and optimization capabilities across the U.K. and global healthcare space. As part of the acquisition, Nautilus will join Accenture’s Health Strategy & Consulting team in the U.K. 

Enhancing Accenture’s Relationship with Oracle Health

Nautilus has vast experience in procurement and implementation of Oracle Health’s Millennium platform (formerly Cerner), a leading provider of digital record systems in the healthcare space. This acquisition allows Accenture to build on its strategic relationship with Oracle Health, further enhancing its ability to deliver a unique end-to-end digital transformation experience at scale.

“With a proven track record in delivering EPR transformation projects, Nautilus has the right talent, digital capabilities, and strategic insights to further harness the data and technology needed to improve the access, experience and outcome of healthcare services. Together, our combined expertise will enable healthcare providers to accelerate their digital transformation, drive better outcomes for all – and help us on our journey to becoming a leading healthcare transformation partner of choice.”

The COVID-19 pandemic shined a spotlight on the urgent need to modernize the nation’s public health system. Despite success in rapidly developing vaccines, the unprecedented public health emergency also exposed significant gaps in U.S. public health infectious disease data collection and analysis methods which are critical for identifying behavioral risk factors and preventive actions.

The Problem

Unfortunately, inefficiency remains a hallmark of the U.S. public health surveillance system due to the following two lingering issues:

• Disparate data collection systems

The CDC receives data from all 50 states and more than 3,000 local jurisdictions and territories. Hospitals, providers, and laboratories use a variety of systems to collect this data which is then reported to state, city, and local public health agencies. The information is then shared with CDC and other federal agencies. In general, each city, county, and state decide what information is collected, as well as how and when it can be shared with CDC.

What’s more, many current systems rely on disease-specific monitoring and manual data entry, which substantially burdens federal data partners. State and local reports to CDC are often delayed because the systems and data are simply not interoperable.

• Antiquated data-sharing methods

While data is increasingly shared via automated, electronic exchanges, some data is still being sent by fax machines, excel spreadsheets, or even by phone. The CDC encourages standardization, but it lacks the authority to receive data directly without establishing a data use agreement with each state and local jurisdiction. 

As a result, the agency must manually clean the data before conducting the analyses needed to provide an aggregated picture of public health. It can take weeks or even months to share the data with public health authorities, providers, and the scientific community,

The key challenge: how to collect and share information more efficiently so that information turns into actionable insights that can shape important public health decisions?

The Progress

The good news is CDC is leading multiple initiatives to make our public health infrastructure more connected and resilient. The CDC’s Data Modernization Initiative (DMI), launched in 2020, is a multi-year, billion-dollar-plus program to modernize core data monitoring and surveillance infrastructure across the public health ecosystem with the goal of enabling faster, actionable insights to support better decision-making. The recently created Office of Public Health Data, Surveillance and Technology will support this effort.  

Four key actions for fully modernizing the public health data infrastructure, and expanding data collection and sharing are:    

1. Adopt a Scalable, Federated Data Mesh Infrastructure

Today’s network of siloed, disease-specific systems creates significant redundancies and inefficiencies. It cannot scale to support the level of data aggregation, access, and speed public health agencies need. 

A scalable, federated data mesh infrastructure would allow federal agencies to curate high volumes of rich, interoperable data across their ecosystems. They could then accelerate their aggregation and analysis, and in turn, their public warnings and outreach, which are critical for fast-moving threats such as infectious diseases. 

By decentralizing data repositories, a data mesh allows those who are most knowledgeable about their data to control it, namely the public health entities functioning as nodes in a network. Via the mesh, the CDC would engage with electronic health records (EHRs), lab reports, genomic sequencing information, immunization, and other records. State and local agencies would then similarly engage. With CDC defining mesh policies and managing the mesh, data can be ingested, cleaned, standardized, and provisioned for use. 

With such a decentralized information technology architecture, federal agencies could also integrate technology to facilitate HIPAA-compliant patient record matching. This could be achieved without creating bottlenecks typically associated with centralized reporting and dissemination. 

Powered by robust metadata, search features and a centralized data catalog, the mesh would enable authorized personnel to effectively find, access, aggregate, and analyze public health data. This information could also be merged to support the principal guidelines for sharing and managing data adopted by research institutions worldwide, known as the FAIR Principles (Findable, Accessible, Interoperable and Reusable).

2. Protect Privacy 

Protecting the confidentiality of patient health information must be a top priority when modernizing public health infrastructure. The data mesh described above can integrate privacy-preserving record linkage (PPRL) technology which allows for data to be linked across different data sets without exposing individuals’ personal information.

PPRL technology maintains HIPAA compliance while enabling the matching of identifiable patient data without compromising patient privacy and confidentiality. For example, PPRL employs hashing to convert variables such as names, birthdates, and addresses into encrypted tokens that preserve the original values.

Linking data at the patient level enables a comprehensive view of an individual’s health, allowing researchers to answer questions that would otherwise require extensive primary data collection or complex data use agreements.

By integrating PPRL with standardized Fast Healthcare Interoperability Resources (FHIR) data components, public health agencies would be able to ingest and collect data from multiple sources and feed it into scalable analytics and modeling tools.      

3. Expand Data Sources

Currently, limited  EHR and social determinants of health data (such as access to transportation, rates of chronic disease, food insecurity, and crime) are interoperable via the established standard – the United States Core Data for Interoperability (USCDI). This data should be augmented by structured health data which is currently siloed in other agency systems including:

• Geospatial data such as walkability and access to care
• Remote-sensing data, such as wastewater testing and satellite imagery
• Mobility data from smartphones, GPS, and sensors along highways 

By layering additional data from siloed health systems and non-health sources, public health agencies could enrich the baseline USCDI data to gain deep insights. Recent efforts demonstrate the value of multilayered data to track the spread of COVID-19 in wastewater samples across the country, understand the impact of social distancing during the pandemic, and predict obesity rates.     

While encouraging, however, these results are limited in scope. Real-time, actionable surveillance at scale is impossible because of the lack of interoperability across data sources. Alternate approaches that bring more data into public health models and simulations must be pursued.

By extending interoperability and connecting the universe of rich, relevant data, public health agencies could boost the accuracy of prevalence estimates, counter-balance biases in traditional data collection, effectively target control and prevention strategies, and better allocate resources.

4. Harness Intelligent Automation 

Modernizing surveillance systems without burdening the public health workforce is a major challenge.

Public health agencies at all levels face a dire shortage of workers, with roughly 44 percent considering leaving their jobs within the next five years. That’s why public health agencies should adopt intelligent automation tools.

Intelligent automation can significantly improve infectious disease reporting by automating the collection and transfer of relevant health information from EHRs. When a health worker records a particular symptom or disease case in a patient’s EHR, the system could automatically send the data directly to CDC, eliminating current administrative reporting burdens. Improvements in the EHR aren’t limited to public health use – intelligent automation systems can also enhance the care provided to patients and decision support provided to providers.

Intelligent automation systems could also scan and interpret lab reports and clinical notes to uncover disease cases that might otherwise elude health officials, and trigger reports to state and local authorities. Additionally, technology learns and adapts. Powered by artificial intelligence and machine learning, these systems can go beyond simple optical character recognition by leveraging natural language processing to understand context, reduce noise, and improve accuracy.

Conclusion   

With a more modernized data infrastructure, public health leaders will be better equipped to identify and contain outbreaks, understand disease burdens, guide policy changes, evaluate and improve prevention and control strategies, and target research investments. The bottom line: enhanced data collection and analysis capabilities are critical to improving our nation’s public health outcomes.

About Kenyon Crowley

Kenyon Crowley, PhD is the Health Analytics Lead for Accenture Federal Services. Dr. Crowley brings nearly twenty years of health information technology expertise to his role. In his role at Accenture Federal Services, Dr. Crowley will help to accelerate the responsible and ethical use of AI and other advanced analytics tools across the federal health sector to help improve the well-being of all people in the country.

What You Should Know:

– Today, Accenture Ventures announced it has made a strategic investment in Virtonomy, a provider of data-driven simulations that use existing patient data and digital twin technology to bring life-saving medical devices to market more quickly. 

– Virtonomy’s digital twin simulation solution enables medical device manufacturers to build model patient virtual environments for device testing at a reduced cost and with less regulatory complexity. The solution is based on an ever-expanding database of real clinical data that reflects factors such as anatomical variability, demographic diversity, and pathological conditions.

Virtonomy plans to use the strategic investment to extend its capabilities to medical technology companies across the globe and power the future of medical device design. The strategic investment is part of Accenture Venture’s Project Spotlight, an engagement and investment program that targets emerging technology software companies that can help fill strategic innovation gaps for the Global 2000.

What You Should Know:

• The benefits of public cloud are promising, as many Amazon Web Services (AWS) and Microsoft customers (both provider organizations and vendors) have noted in previous KLAS research. The first health systems using a public hyperscale cloud provider (e.g., AWS, Google Cloud Platform, Microsoft Azure) to support their EHR environment in the cloud have begun to go live, and interest in this approach is gaining momentum, particularly among Epic organizations. Most who are pursuing this path are just beginning their journey, but some are actively selecting their public cloud provider and planning next steps.
• KLAS recently interviewed 10 organizations who are considering moving part or all of their Epic environment to the cloud. This report shares which public cloud providers they are considering, what Epic environments they may move to the cloud, what goals and concerns they have, and what role third-party firms will play in the transition.

Which Public Cloud Providers Are Early Movers Considering?

This report is the result of a perception study where healthcare organizations shared their decision-making process around moving their Epic environments to the cloud. To gather these perspectives, KLAS used a supplemental evaluation to interview 10 healthcare organizations that are actively considering moving part or all of their Epic environment to the cloud. Data was collected between September 2022 and January 2023.

Key insights and trends are as follows:

1. Microsoft Azure Often a Front-Runner Due to Respondents Already Leveraging Azure or Microsoft Tools in Other Areas: Microsoft Azure is frequently considered in this sample. The bulk of respondents considering Microsoft Azure for their public cloud provider are currently leveraging Azure in other areas; they have Azure workloads set up or utilize Microsoft Office 365. Thus, they want to take advantage of the existing relationship, infrastructure, and expertise (Epic publishes architectural guidance for Microsoft Azure) to move their Epic environment to the cloud. Additionally, some believe the existing relationship will accelerate the contracting process and help them effectively negotiate their contract to manage costs. Scalability is frequently brought up as a concern and is preventing some from seriously considering Microsoft Azure; respondents believe the cloud provider doesn’t support larger organizations as well as AWS does. To address this concern, respondents would like Microsoft to show more evidence of supporting customers their size and to provide professional services that facilitate customer success.
2. AWS’ Epic Expertise and Strong Bench of Technical Resources Drive Considerations: Because AWS introduced broad cloud capabilities to the market relatively early, they are frequently considered in this sample and perceived as being ahead of Microsoft Azure in developing the capabilities healthcare organizations need. Reasons for consideration include existing proof points with other Epic customers, technical expertise (Epic publishes architectural guidance for AWS), and a strong bench of resources that can provide professional services, support onboarding, and help develop organizations’ long-term cloud strategies beyond moving their EHR to the cloud. Among those who likely won’t select AWS, one feels AWS has an immature healthcare focus and hasn’t yet built up clinical subject matter expertise, while another feels the overall cost—when factoring in AWS’ partner costs—is much higher than other similarly structured options. Further, because many respondents already use Microsoft products—including Microsoft Azure—at their organizations, some are contemplating moving forward with Azure to consolidate contracts and vendor relationships.
3. Most Respondents Want to Quickly Leverage Disaster Recovery Capabilities while Moving Their Production Environment to the Cloud over Next Year or Two: Several interviewed organizations feel a sense of urgency to begin transitioning to the cloud, though their reasons for doing so vary. Some want to avoid making significant capital investments in hardware, improve data security, and find alternatives to current hosting options that can’t meet their needs. Most respondents want to use the cloud for disaster recovery within the first three months of contracting with their public cloud provider; this would give them ample opportunity to test the migration process and safeguard against the need to resort

to paper during an outage. Moving to a full production environment is then expected to take one or two years. Beyond moving the EHR to the cloud, almost all respondents plan to take advantage of having Cogito data in their cloud environment. They also anticipate moving other applications (both Epic and non-Epic) to the cloud. Respondents who anticipate taking the longest to move to the cloud say that finding skilled resources, developing a deep understanding of cloud architecture, and consolidating their health system onto one EHR are constraints that will likely slow them down.

• For Most Respondents, Capital Cost Reduction Is the Primary Goal of Moving to the Cloud; However, Uncertainty around Costs Are the Primary Concern: Respondents’ number-one goal in moving their Epic environment to the cloud is to cut capital costs by eliminating hardware investments. Further, public cloud providers’ flexible contracting (which allows organizations to pay for only the cloud services they use) is anticipated to help reduce spending; those considering AWS mention this benefit more frequently than organizations considering other public cloud providers. Other frequently mentioned goals include improved system reliability and uptime and fewer FTEs providing on-premises support. Though capital cost reduction is the main driver for moving to the cloud, cost uncertainty is the top concern. There are many variables that make the true cost of cloud hosting difficult to nail down, and respondents worry their costs could increase in the long run if their cloud environment isn’t architected properly, if utilization isn’t effectively monitored, or if their cloud provider increases the price when the contract renews. Additionally, several respondents (particularly those at larger organizations) are concerned about scalability, as public hyperscale cloud providers are still relatively new to hosting Epic in a production environment. Respondents are also concerned their staff doesn’t have the needed skills to support a move to the cloud.
• Most Respondents Anticipate Using Third-Party Firms to Support Their Cloud Migration; Management Consulting Firms Are Top of Mind: Although respondents plan to leverage public cloud providers’ professional services to move to the cloud, some feel the cloud providers may not have a sufficiently deep bench of resources to meet their needs. Also, the organizations lack the staff and skills to effectively manage the transition themselves—70% feel only somewhat prepared to move to the cloud. As a result, most respondents are considering using a third-party firm to support their cloud migration. They hope these firms will be able to supplement public cloud providers’ engineering resources, assist with training their internal resources, and provide effective processes and road maps during the transition. In addition to using third-party firms, some respondents are currently leveraging firms to help build their cloud business cases and assess their organization’s readiness. Of those who anticipate using a third-party firm in the future, half are unsure which firm they will use. The other half plans to partner with traditional management consulting firms, specifically Accenture, Deloitte, EY, Hewlett Packard Enterprise, and PwC.

What You Should Know:

– Health equity – or the lack thereof – is one of the biggest challenges facing US healthcare organizations today.

– A new report from Accenture, US health inequity: beyond the statistics, analyzes the role US healthcare ecosystem participants can play in addressing health inequities due to race and ethnicity. The statistics are staggering. 

Analysing Key Trends Concerning Health Inequities 

To understand the human and economic impacts of health inequity, Accenture conducted a systematic analysis of peer-reviewed literature and critical opinion leader views to then collate insights from extensive client engagements.Additionally, Accenture spoke with subject matter experts from the life sciences, healthcare and public health industries. Through these actions, one could synthesise health inequities’ leading causes and effects.

Accenture identified five interconnected areas that negatively affect people:

1. Implicit bias: Implicit racial and ethnic biases are unconscious attitudes or stereotypes that affect our understanding of, and responses to, individuals and groups other than our own. These biases erode quality of care and access for underrepresented patients, in turn leading to a lack of trust in, and even fear of, the healthcare system as well as lowering participation by POC in their healthcare journey.

2. Lack of trust and fear: Given longstanding issues with the medical system (such as mistreatment of, and unethical studies among POC in the US), their relationship with the healthcare system is characterized by a general lack of trust and high levels of fear.

3. Access barriers: Access barriers are roadblocks which prevent underrepresented groups from receiving equal access to care such as the affordability of care/medicines, the effects of implicit biases, health education, participation in clinical trials and other barriers such as language or transportation.

4. Uneven quality and experience: Stark differences in the quality of care exist between races. Patients from racial and ethnic minorities frequently receive sub-par care and experience worse outcomes relative to white patients. This uneven quality is directly tied to biases, trust and fear, and access barriers.

5. Racism in R&D/Clinical practices: Lack of representation of people of color (POC) among both patients and physicians, barriers to access and other obstacles like bias, lack of trust and fear, and limited care access all limit the quality of health and research outcomes among affected groups.

According to the report, health ecosystem players’ current efforts to address inequities are disconnected from the daily realities faced by underrepresented communities. Their efforts need to be more relevant, organized, streamlined and cohesive. To achieve health equity, the next generation of actions must consider three core areas that tackle the five key interconnected areas:

1. Mitigate bias in data analytics and algorithms: Clinical algorithms are at the heart of the digital core of Health organizations. They intend to improve accuracy and efficiency but have sometimes proven to widen healthcare disparities. The most cited example is the large commercial health decision algorithm that used healthcare costs as a proxy for health needs, which inappropriately led to Black and African American patients being labeled as “healthier” than equally sick white patients. To advance health equity, data analytics and algorithms must be inclusive, fair, accountable, transparent and easily explainable.

2. Design inclusive products and services: Health equity must be considered from the start of product and service design. Inclusive design methods enable and draw on the full spectrum of human diversity and individual experiences to create solutions. This does not mean that a single product or solution meets every person’s needs. Instead, it means designing different ways for people to receive the same access, experience and outcomes while having a sense of belonging. Considering health equity at this stage encourages better practices, greater accessibility and a more inclusive healthcare environment which drives value for people and ecosystem participants.

3. Create sustainable structural change: To make long-lasting, meaningful change, participants must ensure that their actions bring about structural change. Racism and implicit biases are embedded throughout the ecosystem. Addressing institutional policies such as inclusive hiring practices, the types of partnerships created, and how participants execute clinical treatment and tools will have far- reaching effects on the sustainability of the healthcare ecosystem. Additionally, engraining equity as a core tenant of participants’ foundation will aid in normalizing these activities across the ecosystem.

Lastly, the report includes suggested actions for healthcare payers and providers, along with suggested actions for bio Pharma companies and public health institutions, to mitigate healthcare inequities, as follows:

Mitigating bias in data analytics and algorithms(healthcare payers and providers)

1. Employ critical evaluation when using non-user-generated demographic data: Analyze gaps in data collection and work with local community brokers (like EMTs, social care organizations, food pantries or schools) to inform available public (or purchased) datasets. This will produce more inclusive data around disease onset, hospitalization and adherence to treatment plans. Comprehensive demographic data collection is a health equity priority for CMS.

2. Leverage existing member health processes and outcomes: Stratify them by race, ethnicity and language (REL), sexual orientation and gender identity (SOGI), disability status, rurality and other variables. Acknowledge bias and gaps in data analysis that don’t accurately reflect communities.

3. Implement frequent and standardised audits: Assess algorithmic and data collection processes. Use machine learning auditing techniques to build in representational fairness, counterfactual reasoning, error rate balance and error analysis to account for confounding racial/socioeconomic factors.

Mitigating bias in data analytics and algorithms(biopharma companies)

1. Develop equitable algorithms: Set organisational diversity standards for any data leveraged for any R&D or other algorithmic purposes to ensure that data is more representative and less fragmented. Doing so will also streamline the ability to amalgamate this data for additional analyses on both social and clinical dimensions. Assess algorithms for bias potential/relative degree of inherent bias. Continuously revise algorithms as more diverse data sets become available. Develop non-biased diagnostic algorithms. Also, consider Institutional Review Board-type data auditing boards or 3rd party auditing of data and algorithms to identify risks and bias.

Mitigating bias in data analytics and algorithms (public health institutions)

1. Use data and analytics to enhance decision-making processes: Create public health dashboards that are broadly accessible and communicate metrics stratified by race, ethnicity, gender and geography, focusing on reporting disparities. Invest in automated data capture and reporting that can produce closer to real-time insights to reduce process hurdles and increase the uptake of evidence-based programs by underrepresented communities.

2. Convene ecosystem partners to advance interoperability, such as enhancing the infrastructure for health information advances: Promote data sharing, interoperability and cross-agency/organization partnerships to be more effective in implementing and adopting programs and interventions.

Ensuring inclusive product and service design (healthcare payers and providers)

1. Ensure that cultural competency training, upskilling and credentials are embedded throughout the organization: Help meet cultural needs through third-party partners (e.g., SameSky, Violet Health, Spora Health). Engage community-based organizations and leaders from the beginning. Defer to the community via interviews and keep them engaged in the design experience to create culturally appropriate service designs and facilitate omnichannel care access.

2. Focus on improving the health literacy of members: Create Explanation of Benefits (EOB) guides and claims material simplified at a more accessible reading level with clear, multilingual and culturally tailored content. Payers can also collaborate with third parties that focus on outreach to specific populations (e.g., CityBlock Health).

3. Implement omnichannel engagement strategies: Provide high-tech or analog/lower-fidelity digital alternatives for people to choose from. Providers can co-design experiences with trusted community leaders (like faith leaders, teachers, librarians and EMTs) using community networks for patients through sustainable partnerships with community-based organizations.

Ensuring inclusive product and service design (biopharma companies)

1. Incorporate health equity considerations in early development and at launch planning: Organize a cross-functional team with individuals who represent specific patient populations by demographic, geography, or living conditions. Tailor marketing plans and materials to include diverse perspectives from the voice of the customer research to appropriately reach the target patient population and respective healthcare professionals.

2. Enhance the patient experience across their entire journey: Involve diverse patients and caregivers (across dimensions of age, race, socio- economic status, ability, etc.) in the design of the clinical trial protocol and associated planned experience. Incorporate more holistic segmentation and design mindsets to provide products and services tailored to specific communities thereby extending these thoughtful strategies across more disease states. Engage relevant partners (e.g., community-based, systems-based) to create patient support programs that drive care coordination beyond financial aspects, such as emotional support, transportation, etc.

Ensuring inclusive product and service design (public health institutions)

1. Develop health and life intervention: Develop and implement interventions to sustainably improve underrepresented groups’ health and overall life outcomes. This may also be done through collaborations, such as public-private and community partnerships, providing ridesharing to help with transportation barriers or leveraging funding tools, like state waivers, to approve broader use of Medicaid dollars for social services related to housing, food insecurity, transportation and interpersonal violence.

2. Influence health equity accountability: Use contractual agreements to hold key partners responsible for health equity. For example, state contracts with managed care organizations (MCOs) can ensure accountability for health equity goals and build momentum. States can also increase the emphasis on outcomes-based care as well as support and review to key partners to ensure a health equity lens is being applied at each step of the decision-making process.

Creating sustainable structural change (healthcare payers and providers)

1. Reinvent care delivery: New care delivery models must tie to payment innovation and address patient access and quality needs. Rising patient demand combined with decreasing provider supply will ultimately harm the most vulnerable among us. Health systems need to stabilize the existing workforce, source talent more flexibly, deploy top of license clinicians more effectively (focusing on what they trained to do) and reskill their teams

2. Move from unsustainable one-time philanthropic efforts to scaled social investments: These social investments aim to improve social, economic, and environmental conditions in vulnerable communities while simultaneously creating opportunities for financial returns for investors. These programs, such as Kaiser Permanente’s Thriving Communities Fund, address inequities at the root, generate value for the entire health system and empower underserved communities.

3. Align executive incentives to reducing disparities: Actions such as tying portions of annual executive bonuses to their success in reducing disparities create greater urgency, generate innovative and cross- functional solutions and prioritize health disparity initiatives. These incentives also create a trickle-down effect and permeate the rest of the company’s culture as leadership emphasizes these actions (e.g., SCAN Health Plan)

4. Develop care continuum plans that integrate clinical research: Every patient should have the opportunity to be matched to the best therapy for their personal situation, and that includes clinical research. In addition to clinical research improving adherence rates, underrepresented communities can access previously unavailable therapies.

Creating sustainable structural change (biopharma companies)

1. Ensure everyone can afford and access the latest medicines: Engage in innovative access models—pricing, distribution and contracting—that drive equity in access and reduce affordability barriers and further offset barriers for underrepresented communities through patient assistance programs, bridge programs, free trial offers and co-pay programs. Create new channels for access to services or treatments. Focus especially where communities of color are disproportionately impacted by lack of proximity to care for therapies that require access to a center of excellence (CoE) or academic center’s care. Ensure that access models drive equitable and broad access and that people from different demographic groups know, can access, and use these support programs.

2. Improve clinical trial diversity through multiple actions: Partner with non-traditional organizations such as retail pharmacies and local labs that are entering the clinical trial space to identify trial-ready, diverse patient populations quickly. Consider decentralized and hybrid clinical research methods to lower the barriers to entry that many marginalized communities face when accessing clinical trials. Develop diversity standards to ensure equal representation among investigators. Provide greater optionality for ways patients might engage with clinical trials in certain parts of the protocol. Identify target patient populations based on disease epidemiology and any social determinants of health that exasperate health status.

Creating sustainable structural change (public health institutions)

1. Implement more proactive public and institutional policies: Intentionally think about how underrepresented communities would fare and benefit when advancing regulatory actions, rulemaking, and other policy levers. Analyze past performance of decision impacts on disparate areas beyond health, from labor to demand for social services as part of policy formulation process. Include social risk factors such as dual eligibility, low-income and rural residency as variables in the payment structure so that providers who disproportionately treat these patients are not penalized due to worse outcomes.

2. Create key partnerships across the entire healthcare ecosystem: Create and lead interdisciplinary alliances that address the social determinants of health while including community members and leaders in these coalitions. Payers, providers, and community groups could better drive referral networks and organize tailored interventions if they worked together. Invest in and develop early- stage community-based organizations. Support the growth of early-stage organizations by connecting them with needed resources (e.g., government funding, infrastructure, technology tools).

Staffing levels continue to plague health systems across all of the clinical and administrative areas, with no real signs of easing in the next several years. Employee retention efforts have never been more critical and leaders are looking to technology to help their staff work smarter. While AI promises to be everything to everyone, the use cases where it is successful are actually quite narrow. However, there has been great progress in automating administrative tasks, particularly in revenue cycle operations, that are producing meaningful productivity improvements and easing the workers’ burden. Collecting every dollar counts and these improvements are making a real difference for revenue cycle teams.

Burnout among providers is at an all-time high causing many to leave the workforce and a healthcare staffing crisis. Despite the promise of technology, to date technology has been more of a contributor to provider burnout than an aid. We must adopt health IT solutions with our providers in mind – implementing technologies that automate and augment repetitive provider tasks, freeing up valuable time for our workforce.

A powerful Master Patient Index (MPI) allows organizations to safely and accurately manage data with fewer resources. Many organizations employ a few to dozens of data stewards to eliminate duplications and resolve record overlays. The number of data stewards required can be reduced drastically through a powerful MPI and referential matching combined with machine learning capabilities.

Health IT is a misnomer. Very little of the health IT industry is focused on improving health; instead it is however focused on collecting codes, rather than innovating workflows that enhance patient care and support providers. The burden that the current state of healthcare IT places on providers directly leads to physician burnout and a reduced capacity to deliver quality patient care.

Imagine if a health system could partner with a tech-enabled virtual care team to offload routine and commoditized tasks. This would open up virtual care capacity for mild urgent and chronic issues, while also freeing up office-based providers to focus on more complex patients. This strategy would also improve clinician burnout by making the primary provider the head of a team that could manage a greater number of people, meaning they could increase their own revenue while ensuring better access for their patients.

Leading hospitals and health systems in North America are using virtual care technology that supports an augmented care environment where a virtual team doesn’t replace, but rather, provides support to the bedside team, enabling health systems to reduce labor costs while liberating their bedside nurses to provide elevated, hands-on patient care. For example, telesitting technology allows health operations staff to raise their patient management abilities from one patient per staff member to up to 16 with virtual monitoring. The solution also enables new Virtual Nursing models which provide health systems with a tool to help address clinician burnout and staffing shortages by efficiently leveraging their most experienced nurses to support bedside care teams and enable more efficient clinical workflows. At Trinity Health, falls declined from as many as 62 in the second quarter of 2020 to as few as 28 in the third quarter of 2021. Not only was patient safety better protected through virtual monitoring, but Trinity Health was also able to reduce its costs by $23M by avoiding non-reimbursed services for those injuries.

Hospitals face the challenge of increasing surgical revenue without adding OR staff or facilities. AI-powered automation in perioperative and inpatient settings, coupled with machine learning and behavioral science, allows systems to more efficiently schedule surgeries and reduce the length of stay. This drives OR growth and relieves overburdened staff of manual tasks.

Burnout threatens clinicians’ effectiveness, which ultimately can harm patients and contribute to costly readmissions. Health IT and intelligent automation that safely reduce the cognitive burden that repetitive and menial tasks place on already overloaded clinicians has never been more needed than it is right now. It also helps ensure certain processes are followed consistently and accurately since growing staff shortages force some hospitals to staff entire units or shifts with temporary or traveling clinicians. Technology can give time back to clinicians so they can focus on their patients – going beyond relieving burnout to reintroducing some of the joy in patient care that has been depleted for so many providers over the last several years.

Whether in a clinical hospital setting or a college campus counseling center, provider burnout is a massive problem touching every corner of care delivery. Fostering wellness in healthcare workers is key to addressing burnout, promoting well-being, and building resiliency among providers. A recent survey by CUPA-HR focusing on employee retention finds that supervisors’ top challenges on campuses are filling empty positions and maintaining staff morale, with almost two-thirds (63%) of supervisors indicating they find filling positions very challenging and over half (54%) facing low staff morale. The integration of medical and behavioral health technology for healthcare providers on college campuses can help relieve many issues, including fatigue and burnout, playing a critical role in fusing healthcare and technology that addresses these critical staffing shortages.

With clinician shortages and an overburdened workforce, healthcare organizations are turning towards digital health strategies to help streamline clinical workflows, boost efficiency, and reduce administrative burdens. Solutions such as remote patient monitoring (RPM) can enable providers to remotely manage patients’ health conditions outside the clinical setting, freeing up vital resources. RPM also allows for improvements in medication adherence, streamlining billing and reimbursement while enabling clinicians to spend more time on patient-facing and revenue-generating opportunities. Although there is no perfect solution for all the growing staffing challenges, digital health technology can help healthcare organizations reduce the burden of routine, manual tasks on clinical staff and deliver greater insights that allow them to focus more on patient care and enhanced outcomes.

In a recent survey, 30% of RNs and LPNs said that making a difference in people’s lives is the most rewarding aspect of the job. Yet, we continue to pile more tedious work on their plates, causing more burnout that results in more clinicians leaving the profession. It’s time to ask nurses to do less. By automating routine and mundane tasks, we can free up nurses to deliver more individualized care, spend time with those patients who need it most, conserve human resources, and reduce costs. Thoughtful automation that lets nurses work at the top of their licenses can have a lasting, material impact on attracting and retaining nursing staff.

With nationwide staffing shortages, budget crunches, patient surges, and the heavy burden upon nurses, physicians, and other healthcare workers during the pandemic era, health systems must make smart, well-informed decisions about staffing, resource use, and system capacity. Like evidence-based clinical decision-making, healthcare operations decision-making needs to be driven by innovative use of hard data and intelligent analytics. Healthcare IT tools, processes, and organizational models like the MDClone ADAMS Center help health systems use their human and physical resources wisely to optimize the care they can deliver to their communities while maximizing the support they give to their professional staff and preventing clinician burnout.

Persistent staffing shortages continue to exacerbate clinician burnout as stress and demands on their time mount. Medical NLP is one technology that holds promise to relieve some of that pressure by improving EHRs for their end-users. By turning the unstructured text in patient documents into succinct, searchable summaries, caregivers can have easier, faster access to the relevant information to patient care and spend less time searching for the ‘needle in the haystack’, reducing their screen time and improving the end-user experience.

Clinician burnout, ‘the great resignation,’ and pandemic-induced capacity issues have turned a glaring spotlight on some fundamental issues plaguing our healthcare system. It’s not a matter of ‘working smarter’ or throwing bodies at the problem, but of empowering our clinicians at the point of care. All too often, technology is a hindrance rather than an enabler. Physicians need immediate access to timely, detailed, and accurate information about a patient to inform therapy options. EHRs alone are not the answer, as they are heavily siloed and built for billing. To make deliberate, informed treatment decisions, clinicians need tools that unify and present relevant clinical data in a way that illuminates trends in real-time.

Most people who go into healthcare or behavioral health services are service-oriented, compassionate people. They’re focused on wanting to use their skills to serve people. Anything getting in the way of that happening creates dissatisfaction. Unfortunately, EMRs can increase burnout by forcing clinicians to spend excessive amounts of time navigating their screens and conducting data entry and administrative work. Telehealth is one of the keys to better work-life balance for providers because the setting gives them more control. It can also mitigate burnout by offering clinicians greater flexibility in how they work, enabling them to work on top of their license, and freeing up time to see more patients, all of which leads to improved job satisfaction.

Angie Franks, CEO of ABOUT Healthcare Inc., which provides access center solutions that enable hospitals and health systems to more effectively manage all aspects of patient transfers and optimize access center operations

The application of technology shouldn’t be limited to one health system or system of care. In fact, its impact can be much more profound when it extends to multiple systems under different ownership. The pandemic is a prime example. With it, we saw an exceptional surge in healthcare demand in a compressed timeframe. Moreover, many of these spikes were tied to geography. Information technology can help health systems load balance capacity across extended regional networks to ensure patient demands for care are met. When the boundaries to care are no longer limited to a specific hospital or health system, healthcare becomes more ubiquitous, and providers can ensure prompt access to care at every stage of the patient journey. And compressing the time to care promotes optimal patient outcomes, which is the ultimate objective.

Prior authorization remains a massive administrative burden for payers and providers, despite ongoing improvement efforts. Patients are frustrated by the care delays that result. Clinicians are experiencing burnout due to the extensive amount of paperwork associated with documenting and conforming to health plan policies. CMS aims to improve prior authorization with a recently proposed rule. It’s a step forward to ease some of the daily administrative workloads. As the conversation continues about reducing health worker burnout, more health plans will start by adopting new technologies to comply with upcoming regulations. Still, many will begin to think bigger and use AI and machine learning in real-time to automate prior authorization decisions – resulting in up to a 60% reduction in administrative work.

What You Should Know:

– Accenture published Fast-Forwarding Healthcare through Data Collaboratives Report to discover how best health ecosystem players access data to fast-forward healthcare.

– Data generated by technical and scientific advances, like connected health devices, consumer health and medical-grade digital apps, individual multi-omics profiling data and the move to cloud storage, holds great promise for accelerated healthcare progress and precision medicine, and data collaboratives (DCs) might be the path forward. This report takes a closer look into DCs with a specific focus on oncology, as one of the largest therapeutic areas in terms of industry spending and new medications’ launch pipelines.

Understanding the Potential Data Collaboratives Hold to Generate Value in Healthcare

Life sciences companies increasingly use DCs to access high-quality data from ecosystem partners. The aim is to accelerate research and early development (R&D), clinical development, and post-launch insight generation. To better understand the extent, implications, and potential benefits of DCs, a survey was conducted  among 59 people who are heads of oncology and institutional review board (IRB) members. Of those surveyed, two-thirds already participate in DCs, and the remainder does not. This quantitative research was supported with qualitative research among 18 cross-industry experts (pharma & MedTech, technology service providers, cancer registries, research institutions, patient communities). The research was conducted across the US and the top five EU countries.

Oncology was deliberately selected for a close look into the therapeutic implications of DCs as it is one of the largest therapeutic areas (TAs)3 in terms of industry spending and new medications’ launch pipelines. The outcomes of the findings could apply across TAs. First, the researchers wanted to understand oncologists’ key DC participation drivers—including their preferred ecosystem setups. Then we profiled the key challenges involved in establishing DCs. Challenges to success were assessed and accelerator were looked at, along with, success factors, and strategies vital to effective DC establishment.

Oncologists believe that data collaboratives generate value\

The overall trend was clear: respondents were unanimous in their view that DCs generate significant value for patient outcomes. Nearly two-thirds (64%) agree strongly and 36% agree somewhat. They also generally believe that DCs generate significant value for research outcomes, though the agreement is more muted on this point (38% strongly agree and 54% somewhat agree). The survey indicates that DCs are more significant for patient outcomes than for research outcomes. Although Pharma and MedTech companies were not surveyed, it would be expected  to see significant DC value in the R&D space. While DC participants currently tend to share data more readily with regulatory authorities and clinical stakeholders, they are open to sharing more data with pharma companies in the future.

Technical and human obstacles to data collaboratives: As far as obstacles go, technology and sponsorship seem to be common threads among all respondents. The top five challenges, in order, are:

– Technical integration.

– Lack of sponsor commitment.

– Compliance with federal data protection regulations.

– Lack of trust among participants.

– Data harmonization

Life sciences companies can’t wait to have an extensive data ecosystem at their fingertips. The implications for product development are immense, and DCs represent a particularly attractive opportunity to stimulate innovation and access large datasets from multiple parties. However, establishing a DC is not to be underestimated as a venture. Many of them fail for lack of a solid business case or because the needs and interests of participating parties are not well understood, preventing the realization of cross- organizational benefits. Some fail at the point of conception, while others are unsustainable in the longer term. Yet, the faster and more effectively companies can implement DCs, the greater the competitive advantage they derive. Ultimately, the pioneering insights they obtain could drive improved healthcare—and make it more sustainable.

The report also highlighted pertinent challenges associated with DC’s which are as follows:

Business Challenges: Business challenges associated with DCs apply to both oncologists participating in DCs and those not doing so. High participation costs are flagged by 40% of those surveyed. Another key issue is that for 25% of the sample group, the benefit of participation is unclear – and the absence of a business model to generate a return on investment makes sponsors reluctant to commit. Single-sponsor dependence is also very risky for a DC project. A sustainable business model will generate value and improve revenues, winning over more than one sponsor. The study reveals that prospective DC partners want to use RWD from the collaborative to identify new treatment targets and biomarkers. Oncologists already using data collaboratives find the greatest value in clinical care improvement and decision-making. This dichotomy of interests suggests that a clear understanding of prospective partner needs is essential to attracting the right DC partners.

Data Challenges: Among oncologists spoken to, a hefty 64% of those who participate in data collaboratives have faced obstacles with the data (insufficient variety). Nearly half (44%) of the academic medical centers said that key required data sets weren’t available from the partners in the collaborative (too much data heterogeneity). Nearly a third (31%) said the DC provided poor- quality data. Other challenges included data heterogeneity (more than half (56%) of respondents said data harmonization is one of the biggest challenges). As a consequence, 21% of respondents highlighted the onerous process of agreeing on a common data model and terminology to reduce heterogeneity. Every potential DC partner follows its own standard or has simply adapted standards to some degree—a finding confirmed by our expert interviews. Disparate standards make it difficult to share data usefully between institutions—which applies mostly to unstructured data but not exclusively so. Overcoming these challenges is vital.

Technical Challenges: Almost all (92%) of academic medical centers in data collaboratives have faced technical issues. The main technology challenges involve the technical integration of hospital systems with the collaborative. A third have faced technical difficulties with the DC platform itself or its associated services. The  survey indicates that more than 90% of academic medical centers still use a purely centralized approach for data exchange with the DC. The centralized approach creates lengthy legal discussions on data transfer. When DCs generate copies of data, adherence to data privacy regulations becomes more complex. The approach inherently prevents expansion into certain countries due to local data privacy and security laws that prohibit data copying or transfer. Further drawbacks of the centralized approach include consent management difficulties, double- data entry, and large data transfer costs.

Data Privacy Challenges: Compliance with data sharing or data protection regulations is important. Data privacy issues are more common in Europe than in the U.S—on average, almost seven in 10 of those we surveyed have faced data privacy/legal obstacles. That number rises to nearly eight in Europe. The experience of setting up data collaboratives and enabling data-sharing initiatives across regions has shown that transferring health data across country borders is also an obstacle. For oncologists not currently participating in a DC, data security and privacy constraints represent the top barrier to participation in a DC. In fact, data privacy and security constraints are a hurdle for 60% of potential partners. addressing and implementing technical data security and privacy requirements was a challenge for 21% of our sample group.The challenge is underlined by the fact that 31% of medical centers want better data privacy and security approach.

Partner Ecosystem Challenges: Appropriate partners are, per definition, indispensable to the success of a DC. Potential partners must be identified, approached, and evaluated against a set of criteria. Among our respondents, almost nine out of ten have faced obstacles with the partner ecosystem. In terms of ecosystem partner challenges, a lack of trust among participants was the fourth most mentioned challenge overall among participating medical centers. It was also one of the challenges most often raised by industry experts. Political conflicts and partners’ personal interests could be related to a lack of trust, and respondents mentioned them as important challenges.

Legal Challenges: Research found that 64% of academic medical centers have faced governance and legal challenges. Challenges include the creation of data transfer, usage, and governance agreements and agreeing on intellectual property (IP) and other contractual terms. Industry experts we spoke to confirmed that contractual and governance agreements and signings are particularly onerous hurdles. Yet it is crucial to create an overarching (and binding) legal structure to achieve mutual trust and transparency.

What You Should Know:

– Accenture Federal Services has won a five-year, $628 million recompete of a contract award from the U.S. Centers for Medicare and Medicaid Services (CMS) to continue its work supporting Healthcare.gov. CMS made this award through the Strategic Partners Acquisition Readiness Contract (SPARC) vehicle.

– In 2014, Accenture Federal Services was awarded a prime contract to take over management of the Federally-Facilitated Exchange (FFE). FFE is the backbone of Healthcare.gov, the federal government’s online marketplace connecting people and small businesses with available public and private health insurance options. FFE and Healthcare.gov are central to CMS’ mission in support of the Affordable Care Act to deliver affordable, quality healthcare coverage and services for tens of millions of Americans.

What You Should Know:

– In the IT advisory space, many firms perform well, but the breadth of their expertise varies—some firms offer many services, while others focus on a specific area. KLAS’s latest report examines the breadth of firms’ validated services as well as client satisfaction and performance consistency.

– To provide better transparency into IT advisory services, KLAS has broken them into the following four segments: IT planning and assessment, revenue cycle optimization, clinical optimization, and analytics advisory services. Additionally, this report categorizes firms into four groups based on validated engagements:

1. Broadly validated (at least one validated engagement in all four segments)

 2. Moderately validated (at least one validated engagement in three segments)

 3. Narrowly validated (at least one validated engagement in one or two segments)

 4. Software vendors that also provide IT advisory services

Trends and Insights Regarding IT Advisory Services

Each year, KLAS interviews thousands of healthcare professionals about the IT solutions and services their organizations use. For this report, interviews were conducted over the last 27 months using KLAS’ standard quantitative evaluation for healthcare services, which is composed of 9 numeric rating questions and 3 yes/no questions, all weighted equally. Combined, the ratings for these questions make up the overall performance score, which is measured on a 100-point scale. The questions are organized into five customer experience pillars—loyalty, operations, relationship, services, and value.

Insights into firms that are broadly validated are as follows:

Chartis Leads, Providing Most Consistently Positive Experience; Tegria & Nordic Continue to Deliver Highly Successful Projects amid Growth

Among firms that are broadly validated, Chartis and Nordic provide the most consistently high performances. The majority of Chartis respondents are highly satisfied with the firm’s extensive healthcare expertise, experience, and execution that guide them to success. Most report that they get their money’s worth. Analytics advisory clients also report accelerated analytic capabilities as a tangible outcome. Tegria has grown quickly over the last few years by acquiring firms such as Bluetree Network, Cumberland, and Navin Haffty (who were measured separately in past KLAS research). Since these acquisitions, most clients continue to report successful projects across engagement types thanks to knowledgeable consultants who offer a collaborative partnership.

About 13% of respondents note challenges; a few see gaps in the firm’s insights and question their ability to deliver actionable recommendations. Nordic is highlighted for its high performance, strong partnership, knowledgeable consultants, and quick replacements when needed. Clients report the firm has continued to provide successful engagements while growing significantly in recent years, including acquiring firms like Bails and S&P Consultants.

One respondent (3%) reported dissatisfaction. Impact Advisors clients appreciate the firm’s expertise and high-quality consultants as well as the price and lack of additional costs for scoping work. A little over 10% of respondents feel less satisfied with their experience, citing struggles with staffing and recommendations that weren’t specific enough.

Accenture Provides Value to Large Organizations; ECG Management Consultants Clients Note Inconsistencies in Consultant Quality

Accenture is known for working with very large organizations on complex projects. Clients report positive experiences, noting strengths such as a deep bench of talent and the ability to execute strategies. All respondents also state that there were no unexpected costs. Of the 11 organizations interviewed in this sample, one respondent had a less-satisfactory experience. ECG Management Consultants’ clients appreciate the high-level resources that drive tangible outcomes by partnering with organizations. Some inconsistencies exist in the client experience—15% of dissatisfied respondents mentioned challenges such as miscommunications, insufficient strategic guidance, and consultants who lack needed experience.

Insights into firms that are moderately validated are as follows:

Huron Provides Highly Rated, Consistent Experiences; Galen Healthcare & PwC (Both Limited Data) Partner with Clients & Exceed Expectations

Huron clients view the firm as a flexible, responsive partner that is easy to work with and driven by results. One of the 10 respondents reports being less satisfied, citing a need for better strategic guidance and recommendations. All interviewed clients of Galen Healthcare (limited data) would engage the firm again due to the strong partnership that helps clients complete complicated projects. These highly satisfied clients feel the firm exceeds their expectations in terms of building out reports, hitting tight timelines, and providing software knowledge. PwC (limited data) is similarly seen as a partner and exceeds most clients’ expectations regarding research capabilities, methodology, and ability to work through barriers. Clients highlight access to thought leaders and executive leaders when needed. All six respondents report being either highly satisfied or satisfied with PwC.

Clients See Pivot Point Consulting as a Partner, Note Some Staffing Misses; Deloitte Clients (Limited Data) Feel Value Is Lacking Due to High Cost

Pivot Point Consulting is seen as a partner that offers strategic guidance and a disciplined approach to mapping out projects. All interviewed clients say they would use the firm again and have experienced no unexpected costs. However, 25% of respondents report some dissatisfaction because of inconsistencies with project managers, subject matter experts, and communication. Overall, clients of Deloitte (limited data) don’t feel they receive enough value for what they perceive as an expensive price tag. Primary concerns include the need for tailored recommendations, insufficient strategic guidance, and consultants’ abilities to foster strong relationships with key stakeholders.

Insights regarding firms that are narrowly validated are as follows:

Chi-Matic and Guidehouse (Limited Data) Are Trusted Partners; Clients See Variation in Performance with Prominence and EY (Limited Data)

Started by former Epic employees, Chi-Matic offers Epic expertise and is viewed as a highly trusted partner who responds to clients’ needs, understands the software being optimized, and can identify other high-value areas for revenue cycle optimization. All respondents say they would recommend Chi-Matic and use their services again. Guidehouse’s (limited data) IT planning and assessment and revenue cycle optimization services exceed all clients’ expectations, as the firm pairs clients with seasoned experts who can strategically guide projects.

Healthlink Advisors, Huntzinger Management Group, and Softek (limited data) also perform well in their respective areas. Prominence—founded by former Epic employees—is seen as an expert in the analytics advisory space. Clients note the firm provides strategic guidance with knowledge transfer. 14% of respondents report lower satisfaction and desire more recommendations. In general, EY (limited data) clients feel the firm can provide strong execution during IT planning and assessment projects. Of the seven respondents, 29% are less satisfied with their experiences due to EY misunderstanding their organizations and problems, and another respondent didn’t receive the expected deliverables due to variation in consultant quality.

What You Should Know:

– Accenture, UKG and Ardent Health Services announced a collaboration to help Ardent Health Services improve workforce visibility and agility across its network of 30 hospitals and 200 sites of care. 

– Ardent is using technology to transform its workforce operations and empower frontline talent with more flexible scheduling solutions that support work-life experiences, while remaining focused on providing quality healthcare services.

– The Accenture and UKG collaboration with Ardent is underpinned by an expanded business partnership between the two companies that is focused on helping clients transform how the workforce is managed to create better team member experiences, optimize operations and unlock new business value. 

Today’s healthcare facilities are facing critical staffing problems. Over 19% of U.S. hospitals are experiencing staffing shortages, according to government data posted earlier this year. Caused primarily by the COVID-19 pandemic, nurses and doctors have left hospitals and healthcare clinics because of the dangerous, high-stress conditions. More recently, the U.S. saw rulings and changes that could affect an individual’s access to healthcare – driving more questions and concerns from patients about how they might be impacted. 

With healthcare administrations already overwhelmed, organizations need to leverage intelligent technologies to support patient care while also allowing skilled professionals to dedicate more of their time and energy to cases that demand the full use of their expertise.

Looking for a solution to this ongoing issue, forward-looking leaders in the healthcare space have turned to AI-powered chatbots to alleviate some of the administrative work that burdens today’s healthcare workers. Chatbots function in many areas, including: 

– Directing patients to specific healthcare information they are looking for

– Providing an easy gateway to find appropriate information about insurance services/claims

– Supporting the updating of patient account information

Utilizing conversational artificial intelligence (AI) built within enterprise automation software and with the use of robotic process automation (RPA), these chatbots can be leveraged by healthcare technology systems to create better patient experiences while simultaneously alleviating staff workloads. In Ireland, RPA-powered software robots helped the country’s Health Service Executive (HSE) save 22 thousand hours of work between September and December of 2020 as the country battled the COVID-19 pandemic.

RPA is most easily described as digital virtual workers that emulate and integrate the actions of a human interacting within digital systems to execute a business process. Put simply, the software operates a computer, mouse, and keyboard like a human – but virtually. RPA can enter data into databases, update records, and save files. It is like having a virtual worker – and employees themselves can create robots for specific tasks they find repetitive and time-consuming, such as pulling or updating patient records. The nurses and administrators who find themselves bogged down with repetitive admin work can do more valuable things with the help of RPA, such as interacting with patients and staff. For HSE, RPA was able to process as many COVID cases in one hour as employees previously did in five days while also gaining more efficient and timely data. Partnered with conversational AI, RPA can become a very powerful tool in the healthcare space.

Conversational AI is a type of artificial intelligence that enables consumers to interact with computer applications the way they would with other humans. Conversational AI has primarily taken the form of advanced chatbots, or AI chatbots. Conversational AI allows robots to talk with chatbots using seamless bidirectional communication. With conversational AI, robots and humans are encouraged to interact using natural language to process service requests. This also means that RPA processes can be triggered directly from a chat, which in turn increases operational efficiency. 

Most are familiar with consumer-oriented chatbots, such as those in ecommerce that assist shoppers with finding the right product or aid account holders in updating information. Similarly, chatbots in healthcare can help people navigate healthcare services. They also help with administrative functions like booking appointments, issuing appointment reminders, finding the correct medical forms, filing claims, paying medical bills, and assisting with refilling prescriptions. Importantly, chatbots also provide real-time responses, increasing the likelihood that patients will engage with healthcare services and the number of people who can access services at any one time. With the help of conversational AI, chatbots can capture the context of patients seeking assistance and can provide an intelligent response. 

Max Healthcare, one of the largest hospital networks in North India, used this type of patient-focused automation to alleviate the claims and billing process. With the help of RPA-powered AI chatbots, Max Healthcare was able to fully automate the manual claims process. What typically was a time-consuming process for administrators and patients became simplified and automatic. With the deployment of these robots, Max Healthcare was able to cut turnaround time by 50% and save up to 75% of the time taken to process health scheme data. It has also improved security and compliance while boosting employee experience. By automating a burdensome, frustrating, and time-consuming process for patients, Max Healthcare created faster and more direct results. Patients were left with a positive experience, more often satisfied with the level of care received, and administrators were given time back into their day to focus on other issues at hand.

The virtual care that was adopted during the start of the pandemic is unlikely to go away any time soon. According to a study by Accenture, at least 60% of patients who used virtual care tools said that based on their experience during the pandemic, they want to use technology more for communicating with healthcare providers and managing their conditions in the future. Chatbots are available 24/7 whereas, often, hospital staff cannot be. With RPA, healthcare-focused conversational AI chatbots are personally dedicated to assisting patients throughout their care process at any time. Chatbots can be an instrumental addition to a patient’s healthcare team, reminding patients about their medications, scheduling follow-up appointments, updating any patient account information to reflect their most recent visit, and keeping their data accessible in one central location. RPA technology then seamlessly ensures this information gets into the right hands of doctors and administrators, keeping them up to date and informed on patient needs and activity.   

Healthcare organizations – no matter their specialty, size, or geographic location – should explore how chatbots can help them alleviate traditionally burdensome tasks. Because so many responsibilities can be automated, the focus can still be centered around delivering top-notch patient care around the clock. 

About Jason Warrelmann

Jason Warrelmann is the Global Director of Healthcare and Life Sciences at UiPath. Warrelmann has over 10 years of organic experience in delivering a combination of process and digital innovation horizontally across healthcare.

What You Should Know:

– Virtual reality mental and behavioral health companies BehaVR and OxfordVR today announced their merger, supported by an initial $13M in Series B funding led by Optum Ventures and Oxford Science Enterprises, with participation from Confluent Health, Accenture Ventures, Chrysalis Ventures and Thornton Capital.

– Uniting under the BehaVR brand, digital therapeutic companies BehaVR and OxfordVR will deliver comprehensive virtual reality-based treatments for mental and behavioral health. Clinically validated, immersive, digital treatments will span the full spectrum of mental and behavioral health needs.

Why It Matters

Virtual reality allows clinicians to create multi-sensory patient experiences that the brain processes as real. Operating under the BehaVR brand, the comprehensive VR treatment platform will help providers, payers and employers deliver and increase access to enhanced behavioral care.

These interventions are tailored to each patient’s unique behavioral health needs. Moving forward, BehaVR and OxfordVR will combine to create the largest VR delivery platform for evidence-based digital therapeutics. The companies combined product pipeline will address a wide range of clinical indications, from mental and behavioral health to chronic diseases, reducing the need for additional behavioral health workforce and over reliance on medication as a primary course of treatment.

OxfordVR, whose gameChange product was recently granted FDA Breakthrough Device designation, will treat patients using automated cognitive-behavioral VR therapy. These therapies can be delivered by a wide range of clinical staff, peer group members and at home. The combined company will be led by BehaVR Founder and CEO Aaron Gani. Gani’s leadership team will include psychiatrist, researcher and author Dr. Daniel Freeman, PhD.

Financial details of the merger were not disclosed.

What You Should Know:

– Today Accenture published Digital Health and MedTech – New Signals for Transformation to understand the impact consumers, patients and healthcare professionals’ experiences and expanding care settings will have on the medtech industry.

– In a global survey of Medtech executives, five key trends were identified, including Care Anywhere and Everywhere with over 75% of executives saying that the expanding care settings and models will significantly reorient their firm’s long-term strategy and change business models.

5 Trends Driving the Future Growth of Digital Health & MedTech

“Healthcare is at a crossroads, facing unprecedented pressure and disruption — including affordability challenges, shifting patient expectations, and an increasing deluge of health data,” said Tim Durst, a managing director at Accenture who leads the global medical technology sector in the company’s Life Sciences practice. “With a unique understanding of therapeutics, patients and providers, as well as insights gained from digital devices in the hands of patients, MedTech companies are well-positioned to lead the transformation to digital health. However, companies will need the proper digital foundation to leverage the necessary insights and create a comprehensive digital health solution.”

The report identifies five key trends driving the future growth of MedTech:

1. The Consumer Patient

Healthcare is no longer a one-way interaction with the patient only on the receiving end; it now requires continuous engagement between the patient, provider and payer. Two-thirds (70%) of executives said that the Consumer Patient phenomenon is very relevant and expect it to significantly affect their firm’s long-term strategy.

2. Care Anywhere, Everywhere

As consumers increasingly expect to receive care how, where and when they want, healthcare is increasingly expanding from hospitals to ambulatory and at-home care locations. The MedTech executives interviewed for the report said that while traditional products continue to drive revenues, the expansion of care to new settings is a key part of their growth strategies.

3. The Rise of Digital Health

MedTech company leaders are prioritizing digital health by building capabilities in-house through investments in R&D, technology, digital and new business models; and by making asset acquisitions to expand their pipeline across therapeutic areas and add new capabilities to innovate faster or reach customers in new ways. Nearly all (99%) executives indicated that development and commercialization of Digital Health solutions has accelerated in the past two years.

4. Converging Sectors

The rise of digital in healthcare is also fueling non-traditional deals, with future success dependent upon various sectors converging to develop products and services across the entire care continuum, according to nearly nine out of 10 (86%) executives.

5. New Regulatory Pathways

Digital Health may fall outside the bounds of established regulatory pathways for untested technologies, requiring new approaches from the sector. Most executives (87%) identified government regulations as a threat and disruptor to their business. This may be due to the shifting nature of regulatory guidelines, as regulatory bodies work to keep up with the rate of new and emerging technologies and ensure they are safe and effective.