Posts Tagged ‘predictive analytics’

Mounting Pressure from Value-Based Reimbursement Models Drives Clinical Improvement Strategy at Allina Health System

April 17th, 2018 by Melanie Matthews

Value-Based Reimbursement Models Drive Clinical Improvement Strategy

As a greater percentage of hospital payments are through value-based contracts, hospitals that reduce costs while maintaining quality will survive, predicts Pam Rush, cardiovascular clinical service line program director at Allina Health.

“How do we improve outcomes and decrease costs?” Rush asked participants in the March 2018 webinar, Predictive Healthcare Analytics: Four Pillars for Success. “We need to start to look at the world differently.”

How can we be more creative and do things differently? How can we use different members of the healthcare teams in new ways, such as nurse practitioners or advanced practice providers, she added. In addition, “we need to invest in data analytics and data resources and have data analysts who can pull the information for us so we can find the variation. We need to invest in physician and caregiver time to look at the data, to make changes in how they improve care, to monitor and see what is working and what doesn’t work.”

These four pillars…population health management, reducing clinical variation, testing new care processes and new models of payment, and leveraging cutting edge technologies…have been critical to the work at Allina Health System’s Minneapolis Heart Institute Center for Healthcare Delivery Innovation, said Rush.

In population health management, we’re looking at how can we focus on adherence to guidelines, identify where there are gaps in care and partner with people across the system, primary care and specialists, to improve consistency and adherence to guidelines, she explained.

Allina is reducing clinical variation by looking at unnecessary variations in care where there is inconsistent care without an influence on outcomes.

“We’re also looking at new ways of doing things. How can we use our nurse practitioners, how do we care for patients once they’re discharged from the hospital and bring them back in for clinic visits? It’s really looking at the care model and how we can do things differently to reduce total cost of care,” she said.

In cardiology, there are so many new devices, procedures and techniques to monitor, said Rush, but we need to figure out who are the right providers to do that monitoring, who are the right patients to do these expensive procedures on and who achieves the best outcomes, because we can’t afford to do all of this new technology to every single person.

Allina looks at these four pillars across the continuum. Starting in primary care to partner on prevention strategies, moving to who gets referred to cardiology, and when they’re referred to cardiology, what are the set of tests or treatments and guidelines to adhere to along the continuum to subspecialties, emergency services and all the way up through advanced therapies, such as transplant.

During the webinar, Rush along with Dr. Steven Bradley, cardiologist, MHI and associate director, MHI Healthcare Delivery Innovation Center, shared these four pillars of predictive analytics success along with details on creating a culture of quality and innovation, building performance improvement dashboards, as well as several case examples of quality improvement initiatives contributing to these savings and much more.

Listen to Ms. Rush describe how MHI leveraged an enterprise data warehouse to identify care gaps and clinical quality improvement opportunities.

Infographic: The Current State of Healthcare Predictive Analytics

March 12th, 2018 by Melanie Matthews

Most organizations sit on a wealth of healthcare data but raw data in itself is not enough to drive down costs and reduce risk, according to a new infographic by Advanced Plan for Health.

The infographic examines how to leverage predictive analytics to improve key areas of cost and risk including wellness programs, case management and telehealth use.

Predictive Healthcare Analytics: Four Pillars for SuccessWith an increasing percentage of at-risk healthcare payments, the Allina Health System’s Minneapolis Heart Institute began to drill down on the reasons for clinical variations among its cardiovascular patients. The Heart Institute’s Center for Healthcare Delivery Innovation, charged with analyzing and reducing unnecessary clinical variation, has saved over $155 million by reducing this unnecessary clinical variation through its predictive analytics programs.

During Predictive Healthcare Analytics: Four Pillars for Success, a 45-minute webinar on March 29th at 1:30 p.m. Eastern, Pam Rush, cardiovascular clinical service line program director at Allina Health, and Dr. Steven Bradley, cardiologist, Minneapolis Heart Institute (MHI) and associate director, MHI Healthcare Delivery Innovation Center, will share their organization’s four pillars of predictive analytics success…addressing population health issues, reducing clinical variation, testing new processes and leveraging an enterprise data warehouse. Click here for more information.

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Infographic: Top of Mind for Health IT in 2018

February 16th, 2018 by Melanie Matthews

Cybersecurity, consumer-facing technologies, predictive analytics and virtual care are the technology trends that are top of mind for healthcare IT executives, according to a new infographic by the Center for Connected Medicine.

The infographic examines how these trends may impact the healthcare industry in 2018.

2018 Healthcare Benchmarks: Telehealth & Remote Patient MonitoringOnce 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: Healthcare Analytics and Big Data

April 12th, 2017 by Melanie Matthews

There is an estimated 50 Petabytes of data in the healthcare realm, predicted to grow to 25,000 Petabytes by 2020, according to a new infographic by Oracle. Analyzing this data powers decision-making from preventive care to disease
management to population health.

The infographic examines how data analytics can support the continuum of care and improve the patient experience.

2016 Healthcare Benchmarks: Data Analytics and IntegrationThe 2016 Healthcare Benchmarks: Data Analytics and Integration assembles hundreds of metrics on data analytics and integration from hospitals, health plans, physician practices and other responding organizations, charting the impact of data analytics on population health management, health outcomes, utilization and cost.

2016 Healthcare Benchmarks: Data Analytics and Integration examines the goals, data types, collection processes, program elements, challenges and successes shared by healthcare organizations responding to the January 2016 Data Analytics survey by the Healthcare Intelligence Network. Click here for more information.

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Guest Post: 6 Ways Predictive Analytics Will Move Healthcare Forward in 2016

June 28th, 2016 by Anand Shroff, co-founder and chief technology and product officer, Health Fidelity

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Identifying at-risk patient populations is one way to use predictive analytics to generate rapid returns.

In non-healthcare sectors like retail and manufacturing, ‘predictive analytics’ was arguably the top buzz phrase of 2015. Respected industry analyst Gartner even included predictive analytics in its ‘Top 10 Strategic Technology Trends’ roundup. Predictive analytics have become increasingly important in the healthcare industry, too, as the volume of electronic data grows.

But healthcare organizations have grappled with how to access, analyze and apply their data. Many lack the advanced automated capabilities needed to extract meaning from complex, unstructured data sets from multiple sources. However, it’s crucial to find a way, since the stakes are incredibly high: A McKinsey & Company study estimated that the industry could extract $300 billion in value annually from big data and drive overall healthcare expenditures down by 8 percent.

The key to extracting maximum value from healthcare data sets is to use predictive analytics and cloud-based technologies. By analyzing current and historical data and using the findings to predict future events and trends, healthcare enterprises such as accountable care organizations (ACOs) and others can address the cost-quality equation that is so essential to successful operations in an outcomes-based environment.

The pay-for-performance ecosystem ACOs and other healthcare organizations operate in today demands new strategies to handle bundled payments and population health management challenges, and predictive analytics are tailor-made to produce the insights they need. Using predictive analytics to assess current data sheds new light on the following key metrics:

  • The relationships between cost, quality and patient outcomes;
  • Clinical best practices that drive optimal patient outcomes; and
  • Individual and population-level health risks.
  • By submitting current metrics to predictive analytics, healthcare organizations will gain incredibly valuable insights into how various factors intersect to affect outcomes and which issues they need to address first to drive improvements and value. As they respond to changes in payment models in 2016 and beyond, healthcare organizations will also use predictive analytics to refine their strategies by:

    • Gaining insights into risk factors and how to optimize risk management;
    • Identifying the practices, performers and results that affect organizational performance; and
    • Assessing the impact of ACO reimbursement and bundled payment strategies.
    • Taken together, these are the six ways predictive analytics will move healthcare forward in 2016. By leveraging the power of predictive analytics, healthcare organizations will be able to clearly identify the factors that drive clinical quality and operational expenses. And by applying this information, they can predict and manage clinical and financial performance with greater accuracy. Moreover, they’ll have the opportunity to drive continuous improvement in practices and processes, which will minimize costs while maximizing care quality going forward.

      Healthcare organizations that want to put predictive analytics to work for their operations should consider a two-part strategy that focuses on simple, high-value initiatives first. They’ll need to create an infrastructure that allows them to secure quick wins and then address more complex projects—for example, focusing on revenue improvement by using predictive analytics to proactively manage risk can pay tangible, substantial dividends in the short term.

      Identifying at-risk patient populations in terms of the 30-day readmission window is another way to use predictive analytics to generate rapid returns. Once healthcare organizations have the right processes and practices in place, they can branch out into more complex initiatives like analyzing value-based payment models such as the ACO, episode-based care and patient-centered medical homes. The ability to use discrete and unstructured clinical, financial and operational data to improve performance is the key to success.

      Organizations that embrace predictive analytics in 2016 and beyond will have a key competitive advantage: They will have finally unlocked the value of their data. Predictive analytics have transformed many business sectors in 2015, and 2016 is shaping up to be the breakthrough year for predictive analytics in healthcare, driving better value and outcomes. That’s good news for healthcare organizations and patients alike.

      Anand Shroff

      Anand Shroff, co-founder and chief technology and product officer of Health Fidelity.


      About the Author: Anand Shroff is a co-founder and chief technology and product officer of Health Fidelity. He is responsible for the company’s product strategy and execution and marketing initiatives. He has championed the cause of enterprise performance improvement by promoting electronic capture, exchange and analysis of healthcare data. Prior to founding Health Fidelity, Anand was vice president of EHR and HIE products at Optum. Anand has an MBA from the Haas School of Business at the University of California, Berkeley and an MS in Computer Science from the University of California, Santa Barbara. Anand has an undergraduate degree in Computer Engineering from the University of Mumbai. Connect with Anand on LinkedIn and on Twitter.

      HIN Disclaimer: The opinions, representations and statements made within this guest article are those of the author and not of the Healthcare Intelligence Network as a whole. Any copyright remains with the author and any liability with regard to infringement of intellectual property rights remain with them. The company accepts no liability for any errors, omissions or representations.