Archive for the ‘Data Analytics’ Category

Infographic: High-Assurance Encryption for Healthcare Network Data

November 7th, 2018 by Melanie Matthews

The high-speed networks used by healthcare organizations are becoming increasingly complex. Multiple devices are linked across a variety of network technologies, protocols and topologies. With this complexity comes risk, according to a new infographic by Senetas.

The infographic examines the healthcare data breach landscape and the cost of a data breach.

A New Vision for Remote Patient Monitoring: Creating Sustainable Financial, Operational and Clinical OutcomesAs healthcare moves out of the brick-and-mortar traditional setting into patients’ homes and their workplaces, and becomes much more proactive, the University of Pittsburgh Medical Center (UPMC) has been expanding its remote patient monitoring program. The remote patient monitoring program at UPMC has its roots in the heart failure program but has since expanded to additional disease states across the integrated delivery system’s continuum of care.

A New Vision for Remote Patient Monitoring: Creating Sustainable Financial, Operational and Clinical Outcomes delves into the evolution of UPMC’s remote patient monitoring program from its initial focus on heart failure to how the program was scaled vertically and horizontally. Click here for more information.

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Infographic: 8 Healthcare Analytics Tips

October 29th, 2018 by Melanie Matthews

Many healthcare organizations are drowning in the medical and consumer data flood. According to research by IDC Health Insights, hospitals rank analytics as the top driver for new IT spending. The data and analytics pressures prevalent in healthcare make the industry unique, according to a new infographic by Sirius Computer Solutions, Inc.

The infographic provides eight tips for healthcare organizations to get started.

Stratifying High-Risk, High-Cost Patients: Benchmarks, Predictive Algorithms and Data AnalyticsHealthcare organizations employ a variety of tools and analytics to identify high-risk, high-cost patients for targeted population health interventions.

Stratifying High-Risk, High-Cost Patients: Benchmarks, Predictive Algorithms and Data Analytics presents a range of risk stratification practices to determine candidates for health coaching, case management, home visits, remote monitoring and other initiatives designed to engage individuals with chronic illness, improve health outcomes and reduce healthcare spend.

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Infographic: Resource Allocation in Healthcare Decision-Making

October 3rd, 2018 by Melanie Matthews

Resource allocation methods use mathematical optimization techniques to identify the allocation of healthcare dollars between a set of alternatives, according to a new infographic by RTI Health Solutions.

The infographic examines how different players in the public health and healthcare system benefit from resource allocation methods.

Profiting from Population Health Revenue in an ACO: Framework for Medicare Shared Savings and MIPS SuccessA laser focus on population health interventions and processes can generate immediate revenue streams for fledgling accountable care organizations that support the hard work of creating a sustainable ACO business model. This population health priority has proven a lucrative strategy for Caravan Health, whose 23 ACO clients saved more than $26 million across approximately 250,000 covered lives in 2016 under the Medicare Shared Savings Program (MSSP).

Profiting from Population Health Revenue in an ACO: Framework for Medicare Shared Savings and MIPS Success examines Caravan Health’s population health-focused approach for ACOs and its potential for positioning ACOs for success under MSSP and MACRA’s Merit-based Incentive Payment System (MIPS).

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Infographic: Current State of Healthcare Analytics and Artificial Intelligence

September 12th, 2018 by Melanie Matthews

Some 58 percent of healthcare executives say analytics are an important part of value-based healthcare strategy, according to a new infographic by GE Healthcare and Intel.

The infographic examines where analytics will help, the biggest analytics opportunities and the biggest analytics wins so far.

Health Analytics in Accountable Care: Leveraging Data to Transform ACO Performance and Results Between Medicare’s aggressive migration to value-based payment models and MACRA’s 2017 Quality Payment Program rollout, healthcare providers must accept the inevitability of participation in fee-for-quality reimbursement design—as well as cultivating a grounding in health data analytics to enhance success.

As an early adopter of the Medicare Shared Savings Program (MSSP) and the largest sponsor of MSSP accountable care organizations (ACOs), Collaborative Health Systems (CHS) is uniquely positioned to advise providers on the benefits of data analytics and technology, which CHS views as a major driver in its achievements in the MSSP arena. In performance year 2014, nine of CHS’s 24 MSSP ACOs generated savings and received payments of almost $27 million.

Health Analytics in Accountable Care: Leveraging Data to Transform ACO Performance and Results documents the accomplishments of CHS’s 24 ACOs under the MSSP program, the crucial role of data analytics in CHS operations, and the many lessons learned as an early trailblazer in value-based care delivery.

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Infographic: 5 Ways Data Analytics Can Help the Healthcare Industry

August 22nd, 2018 by Melanie Matthews

Using data analytics, healthcare providers can take charge of the information from multiple sources and convert it into meaningful insights that can lead to timely and strategic decisions, according to a new infographic by Snuvik Technologies.

The infographic looks at how data sources can help deliver next-level insights to patients using big data analytics.

Health Analytics in Accountable Care: Leveraging Data to Transform ACO Performance and Results Between Medicare’s aggressive migration to value-based payment models and MACRA’s 2017 Quality Payment Program rollout, healthcare providers must accept the inevitability of participation in fee-for-quality reimbursement design—as well as cultivating a grounding in health data analytics to enhance success.

As an early adopter of the Medicare Shared Savings Program (MSSP) and the largest sponsor of MSSP accountable care organizations (ACOs), Collaborative Health Systems (CHS) is uniquely positioned to advise providers on the benefits of data analytics and technology, which CHS views as a major driver in its achievements in the MSSP arena. In performance year 2014, nine of CHS’s 24 MSSP ACOs generated savings and received payments of almost $27 million.

Health Analytics in Accountable Care: Leveraging Data to Transform ACO Performance and Results documents the accomplishments of CHS’s 24 ACOs under the MSSP program, the crucial role of data analytics in CHS operations, and the many lessons learned as an early trailblazer in value-based care delivery.

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Infographic: How Big Data Will Unlock the Potential of Healthcare

August 6th, 2018 by Melanie Matthews

The amount of medical data generated each year is rising astronomically. Understanding how to connect that data for new growth opportunities, greater efficiency and better serving consumer needs is a pressing challenge for healthcare organizations, according to a new infographic by Publicis Health.

The infographic examines these data trends and how healthcare organizations can successfully activate data.

UnityPoint Health has moved from a siloed approach to improving the patient experience at each of its locations to a system-wide approach that encompasses a consistent, baseline experience while still allowing for each institution to address its specific needs.

Armed with data from its Press Ganey and CAHPS® Hospital Survey scores, UnityPoint’s patient experience team developed a front-line staff-driven improvement action plan.

Improving the Patient Experience: Engaging Front-line Staff for a System-Wide Action Plan, a 45-minute webinar on July 27th, now available for replay, Paige Moore, director, patient experience at UnityPoint Health—Des Moines, shares how the organization switched from a top-down, leadership-driven patient experience improvement approach to one that engages front-line staff to own the process.

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Infographic: 5 Ways Artificial Intelligence Will Transform Healthcare

July 23rd, 2018 by Melanie Matthews

From personalized medicine to CRISPR gene editing, artificial intelligence (AI) has a revolutionary part to play in tackling healthcare industry challenges, according to a new infographic by AI Business.

The infographic examines the potential for AI to improve healthcare services and reduce costs.

2018 Healthcare Benchmarks: Telehealth & Remote Patient MonitoringArtificial intelligence. Automation. Blockchain. Robotics.

Once the domain of science fiction, these telehealth technologies have begun to transform the fabric of healthcare delivery systems. As further proof of telehealth’s explosive growth, the use of wearable health-tracking devices and remote patient monitoring has proliferated, and the Centers for Medicare and Medicaid Services (CMS) has added several new provider telehealth billing codes for calendar year 2018.

2018 Healthcare Benchmarks: Telehealth & Remote Patient Monitoring delivers the latest actionable telehealth and remote patient monitoring metrics on tools, applications, challenges, successes and ROI from healthcare organizations across the care spectrum. This 60-page report, now in its fifth edition, documents benchmarks on current and planned telehealth and remote patient monitoring initiatives as well as the use of emerging technologies in the healthcare space.

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Infographic: Reducing the Use of Low-Value Healthcare Services

July 16th, 2018 by Melanie Matthews

The United States spends more, both per capita and as a percent of GDP, on healthcare than any other country, yet fails to achieve commensurate health outcomes. One reason for this discrepancy between health spending and outcomes is the significant amount—upwards of $200 billion per year—that the United States spends on low-value care, according to the University of Michigan Value-Based Insurance Design (V-BID) Center.

A new infographic by the V-BID Center provides a list of the top five low-value clinical services for purchasers to target for reduction. The selected services were chosen based on their association with harm, their cost, their prevalence, and the availability of concrete methods to reduce their use.

Profiting from Population Health Revenue in an ACO: Framework for Medicare Shared Savings and MIPS SuccessA laser focus on population health interventions and processes can generate immediate revenue streams for fledgling accountable care organizations that support the hard work of creating a sustainable ACO business model. This population health priority has proven a lucrative strategy for Caravan Health, whose 23 ACO clients saved more than $26 million across approximately 250,000 covered lives in 2016 under the Medicare Shared Savings Program (MSSP).

Profiting from Population Health Revenue in an ACO: Framework for Medicare Shared Savings and MIPS Success examines Caravan Health’s population health-focused approach for ACOs and its potential for positioning ACOs for success under MSSP and MACRA’s Merit-based Incentive Payment System (MIPS).

Get the latest healthcare infographics delivered to your e-inbox with Eye on Infographics, a bi-weekly, e-newsletter digest of visual healthcare data. Click here to sign up today.

Have an infographic you’d like featured on our site? Click here for submission guidelines.

Guest Post: 5 Steps To Prepare for Real-Time Enterprise Healthcare Data

July 2nd, 2018 by Melanie Matthews

The right real-time enterprise data infrastructure allows the information to be routed to a data lake where enterprises can employ modern business intelligence solutions to derive actionable insights.

Recent trends and emerging technologies are converging and a truly real-time enterprise will soon be an achievable possibility. As we move beyond traditional batch data to include streaming data, healthcare systems are seeing an unlimited and unyielding flow of data. This constant flow gives enterprises the ability to act on the information as it originates. Additionally, the right infrastructure allows the information to be routed to a data lake where enterprises can employ modern business intelligence solutions to derive actionable insights.

Of course, not every organization will need to be able to utilize truly real-time data, all organizations need to consider how they can best manage the increasing flow of data. Following are five steps to consider as you develop your enterprise information management (EIM) strategy:

  1. Define/identify business objectives – Is real-time data needed?: While the use cases are innumerable, real-time applications of data by their nature require a much higher level of network resources than data that is sent every hour or every day, as batch processes often are. Consider this: do you need data immediately or is once per hour sufficient? Organizations must first consider how frequently information is needed and then set the strategy.

  2. Find your edge and manage devices: Advancements in integration, messaging software, and Internet of Things (IoT) are building a new edge of the network. Mobile devices in the modern context can be virtually anywhere. To have success organizations need a data and device strategy to ensure that they can “read” the data they need, when they need it. Asset management strategies are also necessary for these devices to ensure that the information on them is controlled, secured, and properly maintained. An increasingly common example of device management at the edge is in healthcare, where tablets and mobile phones are increasingly used at the point of care.

  3. Let Data Streams Flow into Lakes: As organizations gather and use different kinds of often completely unrelated data forms, it makes a lot of sense to create a data lake. Whether this is required goes back to the context of use and the business objective, but in all cases, it is crucial to develop a strategy to consolidate, store, protect and back up the data.

  4. How Do Users Consume the Data?: Information for the sake of information can be distracting. Real-time data is no exception. Again, it is critical that its use be considered in the development of strategy. Let’s use monitoring again as an example. Do users need to know what is happening all of the time, or just when something is wrong, or some other key milestone? If they only need to know at certain points (problem detected, report generated, etc.), what is the best way to relay that information—an alert, a color-coded dashboard? The possibilities are limitless but should reflect a keen understanding of how the information will be used when needed most.

  5. Build in Analytics to Mine That Gold: Information is dynamic and so are the use cases that motivate different users to seek and apply it. For many, the information they gain is descriptive, for some it’s diagnostic in nature, and for others it’s predictive. An example can be found in the predictive analytics used to proactively identify equipment failure and to guide the resulting maintenance and repairs. For others it is prescriptive and informs what is happening currently to help define what should be happening. Regardless, the enormous range of use demands that organizations seeking to benefit from real-time data first establish the infrastructure necessary to run analytics in a way that pulls out actionable, relevant information.

A move to real-time enterprise will require changes to virtually every part of an organization. It will take a great deal of time, attention and hard work; however, the benefits will be significant. The five steps discussed here can help healthcare organizations find and stay on the right path to becoming a real-time enterprise.

About the Author:

Jennifer Schwartz

Jennifer Schwartz is an accomplished professional with special expertise in enterprise information programs, consulting, strategic planning, and mobile solution architecture. She has 18 years of experience with improving operational efficiency, reducing costs, and formulating strategic plans for her clients. As the Enterprise Information Management solution lead for CTG, Ms. Schwartz focuses on business process management and automation, providing best practice guidance, and executing special projects that help transform data into action. Jennifer works across industries, advising clients on the execution of projects to realize efficiencies.

Guest Post: Lab Data is the Missing Link in Healthcare Risk Adjustment

June 19th, 2018 by Jason Bhan, MD

Data informing risk adjustment programs is critical under value-based healthcare reimbursement models.

For health plans, value-based care means a continuous need to innovate and improve their risk adjustment, clinical quality, and care management programs. Unless payers identify and receive the correct amount of reimbursement, it is difficult for them to invest appropriately into member care programs for better outcomes while remaining financially successful.

The data informing risk adjustment programs are critical, as they build the foundation for accurate member risk stratification. In that respect, those data sources are directly related to the correct amount of reimbursement payers receive and can invest in proactive care management. In other words, high-quality clinical data delivered quickly enough for a plan to get a member into a care management program early enough is important to the health of the member and the business. The approach leads to improved clinical outcomes and reduced costs in emergency room visits, hospitalizations and chronic condition management.

Lab Data: An Untapped Resource

To achieve such clinical granularity, at scale, plans can turn to diagnostics—or lab—data. Lab data drives approximately 70 percent of medical decisions and, unlike claims data, is available in near real-time. It also provides an unrivaled level of specificity for clinical conditions. When lab data is integrated into plans’ claims- and chart-based programs, it enables earlier, more comprehensive and accurate clinical insights to benefit care management of both existing and new members. Utilizing the same information that clinicians use to make decisions, within the same timeframe, provides a powerful and unique opportunity to intervene and impact a patient’s health.

What Can Lab Data Do for You?

Expanding and improving their clinical data supply with diagnostics data can help health plans to:

  • Provide historical insights on members where claims are unavailable to improve risk adjustment. For new enrollees, this enables the health plan to get new members into the appropriate care/disease management programs from day one.
  • Serve as an early detection system for care management of all enrollees. Plans can identify patients in need of additional or alternative therapy from lab data earlier than from any other data source. For existing members, the detailed results uncover needs that may have been overlooked based on a claims analysis alone.
  • Identify high-risk members for case management and provider interventions from lab data. Optimized risk adjustment aligns reimbursements to health status, enabling the plan to more heavily invest in member care programs.

Applying AI Solutions

When it comes to gaining actionable insights from diagnostics data, plans can benefit from partnering with healthcare artificial intelligence (AI) specialists in the field. Healthcare AI organizations use techniques such as machine learning and natural language processing—coupled with massive computational power—on big data sets, to make sense out of non-standard, complex, and heterogeneous data.

Healthcare AI, when applied to diagnostics clinical lab data, improves risk stratification by identifying diagnoses earlier in the year versus waiting for the claim or searching charts. Rich in clinical details, it presents a more complete picture of the member’s health. Better risk stratification leads to better care management programs; and successful programs have been shown to reduce costs by targeting those most likely to benefit and keeping intervention costs low.

Dr. Jason Bhan

About the Author: Jason Bhan, MD, is co-founder and Chief Medical Officer at Prognos, an innovator in applying AI to clinical lab diagnostics. More than half of the Prognos team is made of engineers, data scientists, and clinicians. Prognos aims to increase the usefulness of disparate healthcare data to better inform clinical decisions and ultimately improve patient outcomes.