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Healthcare Execs Rank Analytics, Value-Based Payments as Top Challenges

October 10, 2017  |  Heather Landi
Healthcare executives identified clinical and data analytics and population health services organizations as the top challenges they will face in 2018, according to a survey by the Healthcare Executive Group (HCEG).

University of Michigan Launches New Precision Health Research Initiative

October 9, 2017  |  Heather Landi
The University of Michigan, based in Ann Arbor, has launched a new Precision Health initiative in which researchers across campus will combine biomedical expertise with big data and social science approaches to tailor health solutions for the population.

Precision Medicine Programs Becoming a High Priority for Health Systems

October 5, 2017  |  Rajiv Leventhal
A recent survey of health system leaders has revealed that developing a precision medicine program within their organization has become an important priority.

The Texas Hospital Association’s Bold Data Analytics Thrust

October 4, 2017  |  Mark Hagland
Leaders at the Texas Hospital Association have been innovating around data analytics, supporting its advocacy leaders in communicating with state legislators, and also helping hospitals to improve their quality and cost outcomes

Swedish Cancer Institute to Strengthen Delivery of Precision Medicine in Breast Cancer Care

September 15, 2017  |  Rajiv Leventhal
The Seattle-based Swedish Cancer Institute and precision medicine company GNS Healthcare have announced a machine learning collaboration with the aim to strengthen, accelerate, and potentially expand the delivery of precision medicine in breast cancer care.

AT NYU Langone Health, Driving Forward on Technology Innovation at an Enterprise Level

September 14, 2017  |  Heather Landi
At New York City-based NYU Langone Health, healthcare senior executive leaders are leveraging technologies like intelligent automation and machine learning to improve care quality and operational efficiency while also striving to enhance the patient experience.

Survey: Healthcare Execs See Poor ROI from EHRs but Optimistic about Analytics

September 14, 2017  |  Heather Landi
Sixty-one percent of healthcare professionals view the “return on digital investment” produced by the billions of dollars spent on electronic health records since 2009 as “terrible” or “poor”, yet the majority of survey respondents rated analytics as extremely...

Top Hospitals Leverage Data Analytics to Reduce Supply Expenses, Study Finds

September 14, 2017  |  Rajiv Leventhal
U.S. hospitals could reduce annual supply expenses by approximately $23 billion in aggregate through improvements in supply chain operations, processes, and product use, according to new research from Chicago-based Navigant Consulting.

At Conference, Experts Address Implications of Intelligent Automation in Healthcare

September 13, 2017  |  Heather Landi
In order to address transformative issues in the healthcare industry, healthcare provider organizations and payers need to be agile, need to drive disruption utilizing technology, and need to be more analytically-driven organizations.

Artificial Intelligence: The Next Frontier in Health IT? (Part 2)

September 12, 2017  |  Rajiv Leventhal
In Part 2 of this story on AI in healthcare, experts discuss the biggest challenges that in exist in this space today, and if doctors fear that they might one day be replaced.

IBM Watson’s Shortcomings Investigated in STAT Report

September 7, 2017  |  Rajiv Leventhal
An investigation into IBM Watson by STAT News has revealed that the artificial intelligence supercomputer has not lived up to its potential.

CDC Awards $28.6 Million for Data-Driven Opioid Overdose Prevention Efforts

September 6, 2017  |  Heather Landi
The Centers for Disease Control and Prevention (CDC) is awarding more than $28.6 million in additional funding to 44 states and the District of Columbia to support their responses to the opioid overdose epidemic.

How Health IT Tools are Working to Reduce the Prescription Opioid Epidemic

August 30, 2017  |  Heather Landi
Facing a growing prescription opioid crisis, healthcare provider and payer organizations are looking to leverage predictive data analytics to identify patients at high risk of an opioid overdose or addiction to intervene before an adverse event occurs.

Artificial Intelligence: The Next Frontier in Health IT? (Part 1)

August 24, 2017  |  Rajiv Leventhal
As artificial intelligence gains more momentum in the healthcare sector, CIOs' use of these technologies has expanded. And as a result, the industry is now moving quickly, crafting solutions to meet this growing demand.

Sutter Health, Quartet Collaborate on Tech to Improve Mental Healthcare

August 23, 2017  |  Heather Landi
Sutter Health, a healthcare system in Northern California, and behavioral health technology company Quartet, are collaborating to deploy a technology platform that aims to better integrate mental and physical healthcare in Sutter Health’s network.

NIH-Funded Genomic Data Center will Focus on Childhood Cancers

August 22, 2017  |  Rajiv Leventhal
The NIH is spearheading a $14.8 million, five-year effort to launch a data resource center for cancer researchers around the world in order to accelerate the discovery of novel treatments for childhood tumors.

IBM, JDRF Collaborate to Apply Machine Learning to Type 1 Diabetes Research

August 21, 2017  |  Heather Landi
IBM and JDRF, a global organization funding type 1 diabetes research, are collaborating to develop and apply machine learning methods to analyze years of global type 1 diabetes research data and identify factors leading to the onset of the disease children.

How Predictive Analytics Can Help Address the Opioid Crisis

August 14, 2017  |  Sanket Shah, Clinical Assistant Professor, University of Illinois at Chicago
Using healthcare data and actionable analytics could play a key role in helping to combat the opioid crisis.

AMA, Others to Support Machine Learning-based Human Diagnosis Project

August 11, 2017  |  Rajiv Leventhal
The AMA and other major U.S. medical associations have jumped on board to support the Human Diagnosis Project, an initiative that combines human intelligence and machine learning to improve patient outcomes.

Health IT Analytics: ‘More Time Spent Shoveling Coal Than Steering the Ship’

August 10, 2017  |  David Raths
As health systems and ACOs establish the infrastructure to provide actionable data to care managers and front-line clinicians, they continue to struggle with messy data, incompatible systems and a lack of automation.

Leveraging Data and Technology to Advance Enterprise-Wide Pharmacy Operations

August 10, 2017  |  Heather Landi
As the healthcare landscape undergoes accelerating change, almost every clinical and operational area within health systems must evolve to keep pace, including pharmacy operations. One health system is leveraging technology and data to help them better manage...

The Key to the Value-Based Transition: Marrying Clinical and Claims Data, Says One Expert

August 8, 2017  |  Rajiv Leventhal
Dr. Emad Rizk recently was interviewed by Healthcare Informatics about how healthcare organizations are progressing in their shift to value-based care, what the biggest challenges are, and how IT is playing a role.

The shift in the healthcare reimbursement structure from doctors and hospitals getting paid for the volume of services they provide to one in which they get paid for the value of the care they deliver has put many patient care organizations in an unfamiliar position.

For one, many providers still have feet in both of these payment buckets, making it more complicated to engage in risk-based contracts with payers. What’s more, most industry experts would agree that a strong commitment to data and analytics is necessary to be successful in this shift. As such, when the Department of Health and Human Services (HHS) announced a plan two years ago to tie 30 percent of traditional fee-for-service, Medicare payments to quality or value through alternative payment models such as accountable care organizations (ACOs) and bundled payments by 2016, many folks saw that goal as too aggressive.

Nevertheless, at the 2016 HIMSS conference, HHS said that they hit that goal ahead of its target, with an actuarial analysis to back it up. Other examinations, however, have shown different findings. A June 2016 survey from analytics vendor Health Catalyst revealed that just 3 percent of health systems met that target set by federal health officials. Only 23 percent expect to meet it by 2019, just a year after HHS had hoped that half of all Medicare reimbursements would be value-based. That particular survey of healthcare executives represented 190 U.S. hospitals.

Undoubtedly, there is mixed sentiment in the industry regarding two core ideas: how providers truly feel about shifting towards value-based payment models; and how quickly entities can make this transition. Regarding the first point, in a May MGMA (the Medical Group Management Association) Stat poll, only 11 percent of physicians reported having positive sentiment around the shift towards value-based payment models, and 49 percent of physicians are split.

One person who is right at the core of all of this is Emad Rizk, M.D., a seasoned healthcare industry senior executive with more than 25 years of experience working closely with payers and government entities. Dr. Rizk is currently the CEO of Waltham, Mass.-based analytics company Verscend Technologies (formerly Verisk Health). Before that, he was president at McKesson Health Solutions and has been on lists such as the “Top 100 Most Powerful People in Healthcare.” Rizk recently was interviewed by Healthcare Informatics about how healthcare organizations are progressing in their shift to value-based care, what the biggest challenges are, and how IT is playing a role. Below are excerpts of that discussion.

How do you see this shift from fee-for-service to value-based care progressing? At what speed and pace do you see entities making this transition?

Boy, that’s an interesting question and it depends on a lot of people and who you talk to, how they define value and how they define the move away from FFS. I’ll give you an example—if you add a quality metric or if you add a utilization metric, so let’s say no [avoidable hospital] readmissions within 30 days, some people will say that you’ve already shifted from FFS to fee-for-value. If you add certain compliance with drugs or compliance with blood tests, like hemoglobin A1C for a diabetic, they might say that is true fee-for-value.

I think, frankly, that to get to true fee-for-value, you have to have full accountability and risk that will be borne by the provider, which would mean to some extent population risk. So, instead of just adding a few outcomes, they will start taking risk for a specific population of congestive heart failure patients and get a PMPM [per member per month] or PMPY [per member per year] payment for the entire year.

So depending on how you answer it, I know a lot of people say we’re moving very quickly, but I would tell you that it has not moved as quickly as we say in the healthcare industry, and I would definitely know because it cannot really be administered by providers on their own. It needs to be administered by payers, and payers do not have the systems yet to be able to pay for a bundled payment.

I think people just really talk at 20,000 feet, but just think about it in terms of this: if you start to pay a bundled amount for a knee replacement, and that includes everything (orthopedic, etc.), that has to be a single payment, and the provider has to coordinate all those payments. Given the broader definition [of value], we’re probably somewhere between 20 to 30 percent; the narrower definition has been a little bit bipolar, really in the Boston area and in California. And in the middle, there has been nothing.

Regarding the sentiment around shifting to value-based payment models, do you think there’s enough motivation there on the part of payers and providers? Is this an issue?

For most people, their definition of fee-for-value is capitation. You’re shifting risk, and by definition you’re shifting cost. I just had this conversation [recently] with [healthcare research company] KLAS; they were trying to understand what providers can do in terms of taking on risk like payers did. I was trying to tell them that payers have done this for years, they’ve done it for decades, but they’re very rich and have sophisticated and retrospective data on the population. This is why people are leaving the exchanges, because they went in blind with no retrospective data. In the mind of physicians, they have never been trained to be at risk. We’re basically utilization-oriented, meaning you have a headache, so let me do an MRI to see if you have a brain tumor. There are also legal issues if you don’t give that MRI. So physicians will continue to be very resistant.

I don’t believe that they’re ready from a data perspective, either. It’s difficult to take on a patient and be at risk for the outcomes for that patient if you don’t have a strong history to see what that patient has, and you don’t have good coordination around the health system to be able to share that data. If you look at what happened with Illinois BCBS [Blue Cross Blue Shield] and Advocate Health Care when Advocate and went into a fee-for-value arrangement, Advocate would take on risk for a certain amount of patients. But then patients wouldn’t go to Advocate hospitals—they’d leak out and Advocate wouldn’t have that data. There’s not enough data sharing between payers and providers. It’s fascinating to me—and I’ve been having this conversation for over 15 years—it’s unbelievable and it’s kind of sad actually that we’ve not combined claims data and clinical data. I cannot believe that we do not have a unified view. It’s mind-boggling.

What is a potential solution to this data challenge?

You could create as much policy as you can on a national level, but healthcare is regional and it will always be regional. One big solution in which people have been very successful is when you get a large health system and a large health plan that have both overlapping populations. So for example, you’re at BCBS of Illinois and you’re at Northwestern. In this case, 30 percent of the Northwestern patients are part of BCBS of Illinois. So you go to BCBS and say we need you to give us patient data for the last two years and create a benefit design so they do not go anywhere else but us. So there needs to be coordination between the payers and providers, and the providers give the payers back clinical data. It’s not exactly an Einstein solution; it’s pretty simple. Providers get the claims data and populate it with the clinical data. This way, you can identify gaps in care in a much better way than if you just had claims. But you can’t have providers take risk if it’s just a shot in the dark. It’s like me telling you to invest in something but I can’t tell you the past performance of it.

How are stakeholders progressing when it comes to leveraging IT tools for this purpose?

On the provider side, we have a big issue. We have EMR [electronic medical record] companies that do not want to share data. As long as we have Epic and Cerner, if we do not at any point in time begin to interoperate across the EMRs, we’re just kind of going against the wind here. Competitively speaking, if you have an Epic install and a Cerner install, they don’t have an incentive to connect the two. They’d rather just you change Cerner or change Epic so there’s more revenue.

Interoperability will happen eventually with the saturation of EMRs. As payers begin to work with narrow networks and high-performing networks, it becomes in their best interest. An example: a high-performing network, physician group or hospital system generally speaking will have anywhere from 6 to 7 percent improvement of cost versus a non-high performing one—and better outcomes, too. When you’re talking billions of dollars, 7 percent is huge. The payers will have a vested interest and they could split the 7 percent—350 basis point (BPS) goes to providers and 350 BPS goes to the payers. It’s not extremely difficult, they can work together, but it will happen on a regional level.

When you look at some of the alternative payment model goals set by the previous administration, do you think they’re too aggressive or are they attainable?

They’re not too difficult; they should be able to get done, they just have to have the systems [in place]. I think we should continue to push, but I’m glad that the [MACRA] time period has been extended. It’s not very easy to extract. It’s easy for the high-level quality metrics, but the next level of metrics will be complex.


Premier: Analytics Helping Hospitals Optimize Blood Use

July 21, 2017  |  Rajiv Leventhal
An analysis of 645 hospitals revealed that comparative data analytics to drive performance improvement has the potential to optimize blood use across numerous diagnoses.

Report: Clinical Decision Support Market to Reach $4.97B by 2021

July 19, 2017  |  Rajiv Leventhal
A changing healthcare landscape that includes a shift to value-based reimbursement will transform the clinical decision support systems market in the U.S., according to a report from Frost & Sullivan.

Plunging Ahead: Advanced Physician Organization Leaders Move Forward to Manage Risk

July 19, 2017  |  Mark Hagland
For the July/August Healthcare Informatics cover story, advanced medical group leaders share how they are analyzing and using data to feed continuous clinical and operational performance improvement

Baylor, Geisinger Partner on Diagnostic Safety Lab

July 19, 2017  |  David Raths
Baylor College of Medicine researchers are teaming with integrated health system Geisinger to develop a systematic approach to measure and improve diagnostic performance.

On Long Island, Allied Physicians Group’s Ambition to Become a Data-Driven Organization

July 18, 2017  |  Rajiv Leventhal
An operational leader at Allied Physicians Group, with 150 pediatricians and 35 office locations, says that in today’s healthcare market, having a broad-based analytics platform is essential.

A New Survey Finds Ongoing Disconnect over EHR Data’s Availability to Support Revenue Cycle

July 18, 2017  |  Mark Hagland
A new survey by LeanTaaS finds that, while the leaders of patient care organizations are attempting to leverage data and analytics to improve clinical and financial outcomes, they have not yet found EHR data to be effective in doing so

Stanford Machine Learning Group, Digital Health Company Develop Deep Learning Algorithm

July 12, 2017  |  Rajiv Leventhal
The Stanford Machine Learning Group, along with digital health company iRhythm Technologies, have developed a deep learning algorithm for the detection and diagnosis of cardiac arrhythmias, or abnormal heart rhythms.

Report: UnitedHealth Closing in on Deal to Buy Advisory Board’s Healthcare Division

July 7, 2017  |  Rajiv Leventhal
UnitedHealth Group and Vista Equity Partners are nearing a deal to acquire Advisory Board, the Washington, D.C.-based healthcare consulting and technology firm, according to a July 6 report in Bloomberg.


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