PREMIUM REPORT FEBRUARY 2016 THE ANALYTICS CHALLENGE · driven by the steady advance of value-based care and the assumption of greater downside risk by providers, are trends that

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PREMIUM REPORTFEBRUARY 2016

THE ANALYTICS CHALLENGE: Gaining Critical Insight into Risk-Based Models

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FOREWORD

We are in the epicenter of a large collision. In the midst of EHR

implementations becoming stable, organizations are overwhelmed

with the explosion of new technology and innovation, all while dealing

with decreasing reimbursement for care, increasing government

mandates, and an ever-increasing severity of illness of patients in our

facilities due to higher out-of-pocket expenses. While this reality may

frighten many, the answer to most of these problems lies in the use of

advanced analytics tools.

It is my great honor to introduce HealthLeaders Media’s 2016

Analytics in Healthcare Survey findings. The survey this year included

350 respondents who are senior leaders, as well as operations, clinical,

financial, and information leaders. Survey questions included topics

such as the basis for finance and patient data analytics, applications

of large/complex data sets, need for analytics software due to risk

contracts, uses of clinical and financial analytics now and in the future,

financial and clinical data analytics capabilities, and the top analytics

challenges over the next three years.

A clear finding is the three-year plan of how organizations expect

to use clinical analytics: 30% currently leverage this technology to

populate registries, while 43% expect to do so in three years. In

addition, the use of clinical analytics to develop risk stratification is

moving from 41% today to 65% in three years.

Patient data acquisition is one of the new technology frontiers that

is rapidly evolving. Only 17% of respondents currently use data from

patient health monitors as part of their analytics efforts, though 43%

anticipate doing so within three years.

Despite these findings, we have a long way to go. Our ability to move

from descriptive to predictive analytics is still in its infancy. But this

development will take our healthcare systems to the next level. The

great opportunity to leverage financial and clinical informatics, risk

modeling, and disparate data sources are the solutions organizations

are approaching as we all deal with all these mounting issues.

While our struggles of balancing technology benefits with implementa-

tion challenges will continue in an aggressive fashion over the next few

years, the vision of leveraging advanced analytics in healthcare gives

hope and promise for stable healthcare systems—systems that can

deliver the outcomes every patient desires and deserves.

As we become more proactive in our data analytics, our ability to

forecast disease states, outcomes, and therapies positively changes

the way we care for patients. For at the end of the day, whether we

are involved in direct patient care or in strategic planning and support,

all we do is about providing excellent, high-quality patient care in this

country and making it easier to navigate the healthcare enterprise

every day. And avoiding the large collisions.

Advanced Analytics: The Solution to Many Challenges

Stephen Beck, MD, FACP, FHIMSSChief Medical Informatics Officer

Mercy Health Cincinnati, Ohio

Lead Advisor for this Intelligence Report



Table of Contents

Foreword 2

Principal Conclusions and Recommendations 4

Analysis 6

Case Studies 15

Analytics Project Supports ACO Goals of Reduced Costs, Improved Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Analytics Initiative Identifies Gaps in Care, Reduces Hypoglycemic Event Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Data Collaborative Facilitates Integration of Payer and Clinical Data . . . . . .20

Survey Results 23

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Fig. 1: Use of Financial Analytics Now. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Fig. 2: Use of Financial Analytics Within Three Years . . . . . . . . . . . . . . . 24

Fig. 3: Types of Finance Data Drawn On for Analytics Activity Now . . 25

Fig. 4: Types of Finance Data Drawn On for Analytics Within Three Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Fig. 5: Use of Clinical Analytics Now . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Fig. 6: Use of Clinical Analytics Within Three Years . . . . . . . . . . . . . . . . . 28

Meeting Guide 39

Methodology 40

Respondent Profile 41

Fig. 7: Types of Patient Data Drawn On for Analytics Now . . . . . . . . . . 29

Fig. 8: Types of Patient Data Drawn On for Analytics Within Three Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Fig. 9: Current Applications for Working With Large/Complex Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Fig. 10: Presence of Downside Risk Contracts Prompting Need for Analytics Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Fig. 11: Financial Data Analytics Capabilities . . . . . . . . . . . . . . . . . . . . . . . 33

Fig. 12: Clinical Data Analytics Capabilities. . . . . . . . . . . . . . . . . . . . . . . . . . 34

Fig. 13: Top Data-Related Analytics Challenges Over Next Three Years . 35

Fig. 14: Top Tactical Analytics Challenges Over Next Three Years. . . . . . 36

Fig. 15: C-Suite Title Responsible for Financial Analytics . . . . . . . . . . . . . . 37

Fig. 16: C-Suite Title Responsible for Clinical Analytics . . . . . . . . . . . . . . . 38



DATA POINTS

> The top uses for financial analytics now are determining cost of care (68%), cost containment efforts (66%), and financial risk assessment (61%). Responses for population risk assessment (35%) place it last. (Figure 1)

> For financial analytics use within three years, responses for population risk assessment (60%) show the greatest growth, increasing 25 percentage points, followed by financial risk assessment (up 15 percentage points to 76%). (Figure 2)

> Sixty-four percent of respondents say that the presence of contracts with downside risk has prompted the need for or increased their dependence on analytics software or services, with 16% saying that dependence has led them to acquire analytics software or services, and 23% saying they are investigating doing this. (Figure 10)

> Comparing the use of clinical analytics now with clinical analytics use within three years, the biggest response increases are for assessing population health needs (up 27 points to 73%), lowering cost of care (up 23 points to 72%), and developing risk stratification (up 24 points to 65%). (Figures 5 and 6)

The transition to value-based care and the assumption of greater downside

risk has providers looking to analytical tools to better help them understand

the increasingly complex industry they serve. Among the many data

challenges they face are integrating financial and clinical data, improving EHR

interoperability, and analyzing payer- and patient-related data from a diverse

range of internal and external sources.

However, perhaps one of the biggest challenges they confront is balancing

day-to-day operational needs with long-term strategies that are often

resource-intensive and highly changeable. It will require a deft leadership

touch to appropriately serve today’s short-term demands with industry

transformation looming in the not-too-distant future.

IT investment supports multiple strategies. Investing in the IT function

covers a lot of ground for most healthcare organizations—it keeps

information infrastructure up and running so that day-to-day financial

operations proceed consistently and, increasingly, it also supports a growing

list of activities around tracking and managing clinical performance

and outcomes. These near-term demands often consume considerable

HealthLeaders Media Research Editor-Analyst Jonathan Bees draws on the data, insights, and analysis from this report:

PRINCIPAL CONCLUSIONS AND RECOMMENDATIONS



management energy and funding, but they are a necessary part of an

organization’s strategy for maintaining and updating IT infrastructure.

However, in spite of these pressing near-term demands, new investments

must also be made with a long-term perspective, and it is especially critical

that the organization’s IT function is aligned with its strategic direction as

it relates to taking on risk and delivering value-based care. Of particular

importance is analytics, which affects both the nature of the activity that IT

must support and the need for the multiple sources of data that will be the

basis for analysis. Establishing a solid foundation of analytics knowledge,

skills, and technologies will require persistence and a constant revisiting of

strategic priorities—after all, an organization’s strategic plans probably will

be revised more frequently because care models and payment mechanisms

are currently in flux.

Descriptive, predictive, and prescriptive. There are three distinct types

of analytics capability—descriptive (what has happened), predictive

(what will happen, given past data), and prescriptive (predictive plus

proactive solutions). While the majority of respondent organizations

support descriptive analytics, this retrospective view of data, while useful,

will be insufficient to meet the challenges of taking on greater risk and

delivering value-based care in the future. Healthcare leaders need to invest

in predictive and prescriptive analytics capabilities so that they can make

decisions proactively and take preventive action, in both their financial and

clinical operations. Perhaps most challenging of all, the financial and clinical

groups’ analytical needs must be fulfilled in as close to real time as possible.

Balancing short- and long-term priorities. Although IT planning and

investment should take into account both the organization’s short- and

long-term strategy, near-term requirements around industry consolidation,

ambulatory/outpatient expansion, and value-based care initiatives can

create exceptional demands for resources. Organizations must navigate this

delicate balancing act of addressing short- and long-term priorities so that

they are not forced to choose one at the expense of the other. For analytics,

take a close look at evaluating in-house development versus using outside

services—in-house development may consume more human resources than

is sustainable and, ultimately, outside services might offer a faster and more

cost-effective path to bring applications online.

PRINCIPAL CONCLUSIONS AND RECOMMENDATIONS (continued)



The evolution of analytics from mainly functioning as a finance and

administration tool to an expanded role that includes integrating financial and

clinical data continues to gain momentum. Its use and increasing sophistication,

driven by the steady advance of value-based care and the assumption of

greater downside risk by providers, are trends that will likely accelerate in the

coming years.

According to the 2016 HealthLeaders Media Analytics in Healthcare Survey,

for example, the top uses for financial analytics now (Figure 1)—determining

cost of care (68%), cost containment efforts (66%), financial risk assessment

(61%)—are traditional financial analytics uses. Responses for population

risk assessment (35%) place it at the bottom of the list. However, when

respondents are asked about financial analytics use within three years (Figure

2), responses for population risk assessment (60%) show the greatest growth,

increasing 25 percentage points, although it remains at the bottom of the list.

As the industry moves from fee-for-service to value-based care, providers will

likely find themselves consumed with analyzing payer- and patient-related data

from diverse internal and external sources. The need for analytics tools to make

sense of the disparate sources for both financial and clinical data will only grow.

ANALYSIS

The Rise of Analytics and the New Risk-Based ParadigmJONATHAN BEES

Here are selected comments from leaders regarding their strategy for

improving the effectiveness of their organization’s analytics staff, and whether

they will focus on developing in-house talent or look to hire from outside.

“We are looking for better engagement with clinicians; the analytics staff

aren’t close enough to patient care to know what to look for and how to explain

variation. The focus will be on developing in-house talent.”

—Chief financial officer at a medium health system

“We will use both in-house and outside talent. We are creating a culture of

integrating analytics with a strategic mindset of leveraging and identifying

new opportunities for change.”

—CEO at a small physician organization

“We are moving our independent data analytics teams to imbed them

within finance and clinical quality to improve the contextual analysis of the

information.”

—Chief financial officer at a medium health system

“For financial analytics, we will probably hire from outside. For clinical

analytics, we will focus on internal development. The strategy at the moment

involves waiting for the EHR company to develop an effective data mining

product so that we can obtain information for clinical analytics, and then

training someone in how to use these.”

—Chief compliance officer at a small hospital

WHAT HEALTHCARE LEADERS ARE SAYING



“How do we leverage all of that data into a decision-support infrastructure, or

into a customer relationship management type of infrastructure that allows

us to proactively manage the health of that patient in a way that eventually

will deliver the highest outcomes at the lowest cost? And how do we time

the evolution to these models? Because when you think about it, today the

majority of reimbursement is still from fee-for-service,” says Indranil “Neal”

Ganguly, FCHIME, FHIMSS, CHCIO, vice president and chief information

officer at JFK Health Systems, an Edison, New Jersey–based nonprofit

healthcare system.

“It is in our best interest right now from a financial standpoint to have the

visit, to see the patient. But as we transition over to a world of either pay-

for-performance or risk-based contracting, we’re really looking to minimize

the number of physical visits or interactions. And at that point we need to

rely much more heavily on technology to give us predictive intervention

capabilities to say, ‘Hey, I see a trend here; let me intervene now when it’s

much cheaper to intervene than when the patient is more acute and will

present at a care setting.’ ”

Financial analytics. Along with the 25-point jump in population risk

assessment analytics efforts within three years, financial risk assessment

increases 15 percentage points, moving from 61% to 76% (Figures 1 and 2).

Clearly, providers are anticipating that they will need to apply analytics tools

to manage the increased risk associated with value-based care.

As providers undertake contracts

with increasing levels of downside

risk, their need for advanced

analytics to manage decision-

making and monitor results will

also grow. Sixty-four percent of

respondents say that the presence

of contracts with downside risk

has prompted the need for or

increased their dependence on

analytics software or services

(Figure 10), with 16% saying that

dependence has led them to acquire

analytics software or services and

23% saying they are investigating

analytics software and services.

Looking at the types of finance-related data organizations draw on now for

analytics activity (Figure 3), respondents indicate that Medicare/Medicaid

patient claims data (78%), commercial payer patient claims data (67%),

and internal provider productivity data (59%) are the top data sources. The

prominence of the response for payer claims data suggests that providers are

focusing on the relationship between patient care and revenue.

“I think we’re going to have to get much more predictive in using analytics. Historically, we’ve been using analytics retrospectively, so it’s looking at the past and then trying to make some kind of judgment as to how we should manage the future.”

—Indranil “Neal” Ganguly

ANALYSIS (continued)




The ongoing move to value-based care also influences provider thinking when

it comes to the types of finance-related data organizations draw on within

three years for analytics activity (Figure 4). While the top data types remain

the same as for data drawn on now—Medicare/Medicaid patient claims data

(up 5 points to 83%), commercial payer patient claims data (up 9 points to

76%), and internal provider productivity data (up 7 points to 66%)—it is

noteworthy that the largest increases are found on other data types.

Care partners’ cost data (up 24 points to 41%), payer cost data (up 18

points to 56%), and care partners’ provider productivity data (up 17 points

to 36%) experience the greatest increases in response. The results for the

two care partner data types indicate the importance of the role that the

care continuum plays in delivering value-based care, and the degree to which

providers expect to use analytics to evaluate that data.

Clinical analytics. As one might expect, improving clinical quality (85%) is

the top response by a wide margin for use of clinical analytics now (Figure 5).

Identifying gaps in care (65%) and identifying variations in care (56%) place

a step below in usage. And echoing the results for use of financial analytics

now (Figure 1), responses for assessing population health needs (46%) place

it in the bottom half of responses.

Within three years (Figure 6), respondents indicate that improving clinical

quality remains at the top (up 4

points to 89%), while identifying

gaps in care retains second place

(up 11 points to 76%). Tied for

third are identifying variations in

care (up 17 points to 73%) and

assessing population health needs

(up 27 points to 73%). Other notable

increases in clinical analytics

uses are seen for lowering cost of

care (up 23 points to 72%) and

developing risk stratification (up

24 points to 65%). The majority

of respondents expect to use

clinical analytics for a wide range

of applications—responses are grouped in a relatively tight cluster, with all

but one (populating registries) receiving a response of 65% or higher. These

are the kinds of activities that relate to value-based care and the ongoing

transformation efforts within the industry.

In a similar vein, the leading responses for patient-related data drawn on

now for analytics activity (Figure 7) are clinical data from EHR (78%) and

patient demographics (72%) in the top tier, followed by a second tier right

“You don’t want to automate it so much that people don’t think anymore, and now they say, ‘Oh, you know, I’m not worried about that number because if it was high enough, it would trigger my MEWS score and I would see the alert.’ ”

—Stephen Beck, MD




around 50%: aggregated EHR and patient claims data (51%), patient lab and

imaging data (48%), and patient pharmaceutical data (48%).

Looking ahead three years, the leading data types (Figure 8) are the same,

although the response levels are higher, and the gap between top-tier and

second-tier responses shrinks: clinical data from EHR (up 10 points to

88%), patient demographics (up 8 points to 80%) at the top, followed by

aggregated EHR and patient claims data (up 20 points to 71%), patient

pharmaceutical data (up 20 points to 68%), and patient lab and imaging data

(up 18 points to 66%).

The greatest increase in response is in the third tier, where data drawn from

patient health monitors, such as remote telemetry, increases 26 points to

43%. The double-digit growth for most categories is a reflection of provider

anticipation of population health management requirements.

Descriptive, predictive, and prescriptive analytics. For the purposes

of this survey, analytics capabilities are segmented into three categories:

descriptive (what has happened), predictive (what will happen, given past

data), and prescriptive (predictive plus proactive solutions). Respondents

were asked to evaluate their financial and clinical analytics capabilities by

these categories.

For financial analytics (Figure 11),

84% of respondents indicate that

they have descriptive analytics

capability, while 49% report some

predictive capability, and just 25%

have prescriptive capability. Not

surprisingly, a large majority are

able to analyze activities that have

happened in the past. However,

these results indicate that a substantial portion do not even have the ability

to apply analytics to what has happened, calling into question their ability

to make informed decisions for their organization. Of course, only 4% of

organizations with net patient revenue of $1 billion or more lack such basic

descriptive analytics capability for financial data.

While results for the highest level of analytics represents the smallest

segment, it is encouraging that 25% of respondents have prescriptive

analytics, which will be critical in enabling providers to take timely preventive

action in the future through a degree of software intelligence.

Results for clinical analytics follow a similar pattern (Figure 12), with 83%

reporting descriptive analytics, 35% with predictive analytics, and 26%

“I think absolutely the future is in predictive and prescriptive analytics.”

—Sue Schade, MBA




prescriptive. The response for predictive clinical analytics is lower than that

for predictive financial analytics by 14 points, which is perhaps an indication

of the difficulty of applying analytics to patient health.

Consistent with the results for financial analytics, while a large percentage

of respondents (83%) have support for descriptive clinical analytics, this

indicates that a substantial portion do not have the ability to apply clinical

analytics to what has happened, a somewhat troubling finding. Again, this

advanced capability is greater among billion-dollar organizations.

The use of analytics to understand past activity should be the baseline, and

predictive and prescriptive analytics are advancements necessary to move

healthcare forward.

“I think we’re going to have to get much more predictive in using analytics,”

says Ganguly. “Historically, we’ve been using analytics retrospectively, so it’s

looking at the past and then trying to make some kind of judgment as to how

we should manage the future.

“We have to get down to looking at the data at two levels,” he says. “One is

being able to understand the broad trends in our populations, but then take

that understanding and create logic that we can apply at the individual level.

To say, for example, if we see a growing trend in A1c values increasing, we

know we’ve got an increasing problem of diabetes in our community. Now,

how do we take that information

back down to the individual level and

target collection of data in a way

that will allow us to predict who’s at

risk for becoming a type 2 diabetic?

And how do we stop or mitigate

those risks as far in advance as

possible?

“I believe there are indexes that

people are leveraging, such as LACE,

and there are a number of clinical

predictive algorithms that are out

there,” Ganguly says. The LACE

Index identifies patients who are at

risk for readmission or death within

30 days of discharge; the initials represent four factors: length of stay, acuity

of the admission, comorbidities using the Charlson comorbidity index, and

emergency room visits in the past six months. “Where we get challenged is

[that] they’re relatively new. They’re not super-well-tested, so some people

question the reliability of the predictive models and algorithms. But I think

that the evidence base is growing and, as that grows, these models will

become more and more valid.

“They’re not super-well-tested, so some people question the reliability of the predictive models and algorithms. But I think that the evidence base is growing and, as that grows, these models will become more and more valid.”




“Predictive analytics is not a new concept,” Ganguly says. “Clinicians have

been doing this for a long time, such as when they stage pressure ulcers

or other things, it’s sort of a common clinical pathway. But now they’re

becoming much more advanced in their utilization. They’re bringing together

more complex tools. But they’re applying them in the bigger chronic areas—

such as heart failure patients, diabetics, and pulmonary patients—where

you’ve got a high cost of treatment. And if you are able to intervene early

enough, you can significantly reduce the cost of care.”

According to Stephen Beck, MD, FACP, FHIMSS, chief medical informatics

officer at Mercy Health, a nonprofit, Catholic health system serving the

Kentucky and Ohio region, predictive analytics doesn’t have to be a complex

undertaking.

“We do this on the inpatient side to look at trends and the numbers, using a

predictive modeling where you know the MEWS [or Modified Early Warning

System] score. You know that based on certain data points you can generate

a score, and this means that patient is at risk for an event—for example, their

heart stopping for a code blue. We performed this on paper, actually, before

we started doing it electronically. And what we found was you could suggest

a new therapy based on one of those measures that would prevent a code

blue,” he says.

“We’re doing more and more of this,”

Beck says. “I believe that the biggest

challenge still comes back to, How

do you address those types of tools

with workflow? Because we know

when you can automate it to the

point where you don’t have to think

about it, then you tend to have good

outcomes. The problem with that

is, you don’t ever want to take the

human brain out of the equation.

“You don’t want to automate it

so much that people don’t think anymore, and now they say, ‘Oh, you know,

I’m not worried about that number because if it was high enough, it would

trigger my MEWS score and I would see the alert,’ ” he says.

“I think absolutely the future is in predictive and prescriptive analytics,” says

Sue Schade, MBA, FCHIME, FHIMSS, LCHIME, chief information officer

at the University of Michigan Hospitals and Health Centers, which is part

of University of Michigan Health System, a nonprofit academic healthcare

system that serves the Michigan and Northern Ohio area. “For example, we’ve

done some predictive analytics around hemodynamic instability. And I would


“As you’re moving toward value-based medicine, or risk-based contracts, you have to figure out how to take better care of your patients. And that’s a very complicated task.”

—Stephen Beck, MD



say that’s definitely in the realm of predictive and prescriptive analytics in

terms of evaluating patients that are decompensating, and determining what

we can know in advance and being able to address it.”

Analytics challenges. The top three data-related challenges respondents

expect to face over the next three years (Figure 13) are integrating internal

clinical and financial data (54%), establishing/improving EHR interoperability

(47%), and integrating external clinical and financial data (41%). These data-

related challenges involve data that originates from multiple sources, which

adds a degree of complexity for providers.

Beck says, “When it comes to using analytics to manage populations, you

may have an empaneled patient that seeks care elsewhere, even though I’m

the PCP. How do I get that information? It’s fine if it’s part of my network on

my EHR, but if it’s outside, how do we get that information back in a timely

fashion and in a way that feeds back into the decision support that I have?

“As an example,” he says, “we’ve worked with Walgreens in our area to ensure

that if they’re giving an immunization, that message is coming back to us so

that we know and can have a record updated. There’s no second-guessing if

we have to do an outreach to the patient regarding a pneumococcal vaccine.

“But I believe the bigger issue is the disparate data systems, the disparate

electronic records in our

communities,” Beck says. “We’re

doing everything that we can to

try to integrate the pieces into

an enterprise data warehouse to

help pull those information pieces

together, yet make the decision

support that comes back to a doctor

much smarter. And it may be as

simple as taking CPT category

II code data or other billing data

directly from the payer. The payer

states that the patient had that

pneumonia vaccine done because

they paid for it.”

Ganguly says he sees interoperability as a lesser problem compared with the

others on the list. “Interoperability is, to some degree, more of an annoyance

than a disaster point. We can exchange data pretty well with most other

systems. I know many of my colleagues are all participating in a variety of

different types of health information exchange initiatives. I think what we’re

not seeing, though, is how we’re getting value out of the data exchange. So


“I will tell you most CIOs kind of cringe at the term big data because it’s one more term that’s taken hold within the industry that everybody grabs on to but probably doesn’t really understand.”

—Sue Schade, MBA



exchanging the data, or aggregating it some way, is less of a problem than

figuring out how to get value out of it and consume some kind of output from

the aggregated data.”

Schade explains that certainly one of the big challenges organizations face is

a multitude of priorities. “Organizations have been very focused on the core

work of EHR, operationally, replacing and installing new systems, and that has

consumed a tremendous amount of IT time. Also, we focus on meaningful use,

and we had to get ready for ICD-10. And during this period I have seen from

an operational perspective just a real increase in the demand around having

good data and being able to do reporting. And I think a lot of organizations

are not necessarily planning ahead for this.

“At the same time,” she says, “there’s a growing demand for deeper analytics

and the emergence of big data—which, I will tell you most CIOs kind of

cringe at the term big data because it’s one more term that’s taken hold

within the industry that everybody grabs on to but probably doesn’t really

understand. And so CIOs like myself are working to make sure that we

are very effective in that core reporting and then—looking more broadly,

especially in an academic medical center—as to what our overall analytics

platform needs to be when you take into account, for us, the tripartite mission

of clinical care, research, and education.”

Tactical challenges. Selecting

the top three tactical analytics

challenges they expect to face

in the next three years (Figure

14), respondents cite a top tier of

overcoming insufficient skills in

analytics (45%) and the need to

deliver timely analysis (45%). The

second-tier responses cited by about

one-third are insufficient funding

in light of other priorities (35%),

picking the right platform for data

and analytics (33%), and insufficient

staff (32%).

Some of the challenges relate indirectly or directly to financial resources,

while others are more closely tied to levels of expertise. Note that the need to

deliver timely analysis is a universal challenge for all industries, although it is

perhaps more acute in healthcare.

Schade says that larger organizations often face challenges related to the

breadth of their organizational data footprint. “I think that you probably have

a lot of organizations similar to ours that have various siloes of data, various


“Exchanging the data, or aggregating it some way, is less of a problem than figuring out how to get value out of it and consume some kind of output from the aggregated data.”




data warehouses that different parts of the organizations have developed,

with the result that you end up with a more federated type of architecture.

Certainly some organizations have been ahead of the curve and have been

able to develop a more centralized platform.”

Ganguly cites the sheer volume of data available for evaluation. “The amount

of information available is tremendous, and it’s growing exponentially.

The [number of] savvy members of the end-user community, whether it’s

clinicians or financial types, is growing, and they understand that there’s

more they can do with the information. So they’re looking for more reporting,

they’re looking for more tools. Our resources are traditionally limited, so how

do we prioritize? You can’t meet all of the demand. So that’s one challenge.

And then I think the other challenge, and part of this is a demand control

strategy, is how do we begin to educate our data customers to handle some

of the basic analytics themselves?”

The human element. While it may be tempting to view analytics as a

panacea for the many challenges of value-based care, there are some aspects

of healthcare transformation that are resistant to analytical approaches.

Consider, for instance, the human element.

Beck summarizes it this way. “As you’re moving toward value-based medicine,

or risk-based contracts, you have to figure out how to take better care of your

patients. And that’s a very complicated task. The reason it’s so complicated

is you’ve got a complicated population—some respond well to verbal

communication and will do everything the doctor asks them to do; some will

take their medication on time and others will not.

“Using analytics, how can you really determine the difference between a

compliance issue—so I’ve ordered a test and the patient just hasn’t had

it done—versus not ordering the test at all because I missed it or a staff

member missed it? It requires taking the combination of decision-support

and active-management tools that are at the point of care, and outreach

tools that are population health–based. And not just implementing them, but

implementing them in a way to make sure that we’re getting the outcomes

that we’re really looking for.”

Jonathan Bees is research editor-analyst for HealthLeaders Media.

He may be contacted at [email protected].




CASE STUDY 1

Analytics Project Supports ACO Goals of Reduced Costs, Improved Outcomes

To support its population health

strategy, JFK Health began an

accountable care organization called

JFK Population Health, LLC, as part

of the Medicare Shared Savings

Program.

Before the ACO went live in January

2014, JFK Health’s IT department

began, in October 2013, preparing

to provide analytics support for

the initiative. Since that time, the

department has focused mainly on

building an analytics interface and

establishing a data repository.

“The IT department viewed it as

a good chance to get exposed to

accountable care and some of the

concepts around care coordination,

and how we can support the hospital

and physicians to make sure that we are identifying gaps in care, gaps in

preventive care, and driving to reduce things like unnecessary readmissions,”

says Indranil “Neal” Ganguly, FCHIME, FHIMSS, CHCIO, vice president and

chief information officer at JFK Health.

ACO integration. JFK Population Health has a two-pronged management

structure for facilitating the integration of the ACO into the parent

organization, based on a Finance Committee and a Quality and Operations

Committee. At one point, there was also an IT Committee.

“We used to have an IT Committee, but we disbanded it because the

physicians didn’t really get into the technology piece of the process. So we

just do that on the back end through the hospital,” says Ganguly. “The Finance

Committee requires data because ACOs have a gainsharing component

to them, and they’re looking at the model by which any gains would be

distributed back to the physicians who are meeting their care objectives. The

Quality and Operations Committee is looking primarily at how well we are

optimizing the outcomes for the patients.”

Early challenges. The first year of the analytics project was mainly

concerned with building the infrastructure for data collection and

JFK HEALTH JFK Health is an Edison, New Jersey–based nonprofit healthcare system that serves the central area of the state. It owns and operates JFK Medical Center, a 498-bed acute care hospital, as well as the JFK Hartwyck Nursing and Rehabilitation Centers and the JFK Johnson Rehabilitation Institute, which feature inpatient and outpatient rehabilitation centers and nursing facilities. The organization has approximately 950 medical staff. In 2014, the system reported $514 million in net patient revenue.



CASE STUDY 1 (continued)

aggregation. This aspect offered a number of challenges due to the disparate

systems used by the various members of the ACO.

“We began with a focus on data collection, and this year is the first we’re

actually producing measurements for the providers to look at in terms of

improvement,” says Ganguly. “Collecting the data is quite a challenge because

of the nature of the physician community and the number of systems in use

there. Some physicians are capturing the data electronically to interface to

their systems. Others are capturing it via some level of manual abstraction,

either at the practice or using hospital resources. Ultimately, it’s all being fed

into a repository where we’re running the analytics and then looking at how

the individual practices are doing against the 33 ACO quality measures from

CMS.”

EMRs and data flow. Inevitably, building the data collection infrastructure

was primarily focused on the ACO physicians’ EMRs. Besides facing the usual

EMR compatibility issues, the IT department also found that not all EMRs

were being used to the fullest extent of their capabilities.

“Even though most of the physicians had EMRs, they weren’t all set up to

capture all of the data. Oftentimes they were just set up in a default manner

and they were capturing enough basic data for the physician to bill properly,

but not necessarily capturing all of the data they would need to manage the

care of the patient.”

Multiple data sources. The data collection initiative is not limited to the ACO

alone. JFK Health is building an enterprise data repository that encompasses

ACO provider data, inpatient data from JFK Medical Center, and data from an

approximately 30-hospital health information exchange in which JFK Health

participates.

“We don’t want to be narrowly restricted to what we know about a patient

from their interaction in our environment alone. Because patients are fluid,

they may come to JFK for a certain thing but then they sprain their ankle in

front of a neighboring hospital, they may go there. With an ACO, the primary

care physician especially needs to know what’s going on with that patient.

But if it’s outside of the JFK ecosystem, how do we report that data?”

Physician workflow. There is also a human element to establishing the

analytics platform. While the ACO’s physicians are organized around a

standard set of data collection practices, they also have to adapt to a new

workflow.

“Part of the challenge is just adoption,” says Ganguly. “Obviously, when you’re

first establishing something like an ACO, it’s not the physicians’ entire panel

that’s involved. So they’re delivering care along a certain path for their entire

patient base, and now they have to take a new path for some subset of their

patients because they are enrolled in an ACO.




“How do you build that into their workflow in a way that doesn’t impede

productivity? Some have staff in the office who will look at the data and prep

the physician in advance; other physicians are more hands-on, looking at it

themselves. But [for] the ones that are able to look at it and really understand

it, then the only question is, are they closing the care gaps effectively?”

Work in progress. While basic reporting on the 33 CMS quality measures

is now active—physicians are able to see their performance compared

against their peers within the ACO as well as against national averages—the

analytics initiative is still in the early stages of development, says Ganguly.

“It’s actually one of the challenges we see across the industry when you get

to analytics. If you don’t have the data to begin with, how do you really build a

true analytics environment? And right now, we have so much data, it’s the old

conundrum—we have tons of data but not enough information. And so the

key is we need to collect usable targeted data at the front end that feeds the

necessary analytics to drive quality and cost control, which is where we really

want to go.”

—Jonathan Bees



At Mercy Health, low blood sugar

event rates were running higher

than organizational expectations,

presenting a potential threat to

patient health and outcomes.

“In the fall of 2013, we identified that

there was an issue relative to a large

number of hypoglycemic events,” says

Stephen Beck, MD, FACP, FHIMSS,

chief medical informatics officer at

Mercy Health. “And so we created a

multidisciplinary team that included

nursing, pharmacy, clinical content

or nursing informatics, as well as

physician representation to examine

where our care gaps might be that

were creating the issue.”

Mercy Health captures and tracks reportable events using a third-party

system. Beck says, “The analysis process initially started with our automated

reporting system for reportable events, and out of that data we then used

analytics to determine that there were a higher than expected number of low

blood sugar events.

“There is also a self-reporting aspect to this, so that any time a staff member

notes or considers a risk to the patient, or a potential harm for the patient,

they can denote it very quickly through this reporting package. And then it

gets assigned as a task, because one of the ways that my team uses this is

when a clinical person denotes a risk, we then try to identify a cause.”

Multiple events per patient. Once Mercy Health determined that

hypoglycemic events were running higher than expected, the organization’s

own analytics group, CarePATH Clinical Solutions, began a deep dive on the

data. One of the first revelations was that the high number of reportable

events was being driven not only by the number of patients with single events,

but also by the number of patients having multiple events.

“That’s a multiple event diminishment,” says Beck. “If a patient had a single

MERCY HEALTHMercy Health is a nonprofit, Catholic health system that has 23 hospitals, eight senior housing facilities, and seven home health agencies serving the Kentucky and Ohio region. Formerly known as Catholic Health Partners, the organization has approximately 450 locations providing care and has over 32,000 employees, including over 1,300 employed physicians. Annual net patient service revenue was $3.8 billion in 2014.

CASE STUDY 2

Analytics Initiative Identifies Gaps in Care, Reduces Hypoglycemic Event Frequency




event, that was categorized, but we also looked to see which of those patients

had double events, if they had two low blood sugar readings. And then we

looked to see which ones had three, and which had four, and which had five

and six, and so forth, all the way up to 10. And what we saw is that we were

having significant numbers of four and five events per patient.”

There were two types of hypoglycemic events studied in the initiative. Critical

low blood sugar events, which are defined as having a blood glucose level of

below 50 mg/dL, and below normal events, which are a blood glucose level

of between 50 and 70 mg/dL. Blood glucose information is tracked in the

patient’s EHR.

Identifying the cause. After studying both the critical and below normal

blood sugar level events reported in the EHR, the analytics team came to an

interesting conclusion. The main culprits for the problem were the workflow

for ordering insulin and a disconnect in the process for addressing low blood

sugar monitoring and medication.

Beck describes the workflow issue: “Clinicians were ordering insulin, and not

placing orders for the ‘what if.’ And in this case, the ‘what if ’ was, if there’s

low blood sugar, what do you do? At that time, we had an order panel that

basically said if you order insulin you should order this panel as well, which

says check the blood sugar every so often, and if it goes below this level, then

administer this medication, stop the insulin, and so forth. We’ve always had

these, but they weren’t linked together. And so that was a simple observation

that we were then able to identify and fix, and in February of 2014 we added

this panel and embedded it inside our order set where you order insulin.

“We also identified an additional gap that when a provider ordered insulin as

a single line item and not as part of an order set, there was the possibility that

we were going to miss some additional patients. So we developed an advisory

a few months later, in June of 2014, so that if this treatment panel wasn’t

ordered along with the insulin you would get an alert, basically automating

the ability to order that panel.”

The results. Adoption of the new protocols produced almost immediate

results. Comparing first quarter with second quarter 2014, total critical low

blood sugar events declined 12.0% (916 versus 806 events) and the number

of patients experiencing more than three critical events declined by 12.6%

(190 versus 166 patients). Likewise, total below normal low blood sugar

events declined 7.3% (3,322 versus 3,081 events) and the number of patients

experiencing more than three below normal events declined by 26.1% (1,109

versus 819 patients).

By project end in fourth quarter 2014, total low blood sugar events had been

reduced by 70% compared with the first quarter results.

—Jonathan Bees



CASE STUDY 3

The Michigan Data Collaborative is a

nonprofit organization that maintains

a statewide database of multi-payer

claims and provider EHR data.

Established in 2008, the organization

is based at the University of Michigan

Health System and falls under the

umbrella of the health system’s IT

department. Data management

and analytical services for MDC are

provided by IT department employees.

MDC was created in partnership with

Blue Cross Blue Shield of Michigan as

part of an effort to foster population

health IT infrastructure and improve

care delivery in the state of Michigan.

MDC’s main function is to provide data

and analytics support for healthcare

transformation initiatives across the state, primarily working in

conjunction with the Michigan Primary Care Transformation (MiPCT)

demonstration project, a Center for Medicare & Medicaid Innovation

project that is part of its Multi-Payer Advance Primary Care program.

According to Sue Schade, MBA, FCHIME, FHIMSS, LCHIME, chief

information officer at University of Michigan Hospitals and Health

Centers, “The Michigan Data Collaborative, or MDC, is part of a

relationship we have with Blue Cross Blue Shield of Michigan that goes

back to 2008. Originally, it was established to create a consolidated

claims database for payers in Michigan, and over time we’ve done

additional work on it to include clinical data. It’s a really important

foundation for improving healthcare in Michigan.”

Funding for MDC is project based, currently coming from the MiPCT

project (which itself is funded in part by the CMS Center for Medicare

& Medicaid Innovation), participating payers such as Blue Cross Blue

Shield of Michigan, and the state of Michigan.

UNIVERSITY OF MICHIGAN HEALTH SYSTEM

The University of Michigan Health

System is a nonprofit academic

healthcare system that serves the

Michigan and Northern Ohio area.

It has three acute care hospitals,

40 outpatient locations, 140

clinics, and more than 26,000

faculty, staff, students, trainees,

and volunteers. Its six specialty

centers provide care for cancer,

cardiovascular, depression, eye,

diabetes, and geriatric patients.

In 2014, the system reported

$2.5 billion in operating revenue.

Data Collaborative Facilitates Integration of Payer and Clinical Data




Organization partners and mission. MDC unites payers and providers in

sharing claims and EHR data, which is then aggregated and made available

to physician organizations participating in the MiPCT initiative. There are

currently five payers in the program: Blue Cross Blue Shield of Michigan,

Blue Care Network of Michigan, Medicare, Medicaid, and Priority Health, a

nonprofit health plan. On the provider side, there are currently 37 physician

organizations. The repository contains data on approximately 4 million

covered lives, and has information on roughly 1.3 billion medical claims

processed over the last five years.

Schade says that the MDC currently has the following core objctives:

• Create a full picture of care—regardless of payer

• Measure population-wide quality and outcomes

• Identify high-risk populations with chronic and comorbid conditions

• Help identify interventions and best practices that work

• Help our partners identify and track cost reduction opportunities

“When Blue Cross initially approached us—I wasn’t here at the time—this

partnership was about establishing a consolidated claims database for all

payers,” says Schade. “At the beginning, it focused on claims data for five

payers in Michigan. However, ultimately, they wanted to combine clinical

data with claims data to show whether patients’ health was actually

improving versus only seeing if a patient had a recommended test or visited

a physician. As the payers have evolved, they are now looking at value and

outcomes more. But I think even what we were doing seven years ago was

fairly leading edge.”

Dashboards and reports. MDC provides a broad range of dashboards

and reports based on the integration of payer claims and clinical data. In

addition, Truven Health Analytics, which also participates in the initiative,

contributes data on risk scores, standard costs, and admission information.

It had initially assisted in aggregating the data, which MDS now handles

in-house.

The MDC repository tracks chronic conditions such as asthma, attention

deficit hyperactivity disorder, chronic kidney disease, chronic obstructive

pulmonary disease, coronary artery disease, diabetes, chronic heart failure,

hypertension, and obesity. It records utilization rates for the emergency

department, as well as admissions and readmissions activity in general. It

monitors quality measures for adult chronic care, adult preventive care, and

pediatric care. And it tracks 15 electronic clinical quality measures.




15 electronic clinical quality measures

Diabetes - HbA1c poor control

Diabetes - HbA1c control

Diabetes - LDL-C control

Diabetes - blood pressure control

Asthma action plan

Hypertension - blood pressure control

CVD - blood pressure control

CVD - LDL-C control

BMI

Tobacco use

Colorectal cancer screening

Asthma action plan – pediatrics

BMI – pediatrics

Tobacco use – pediatrics

Depression screening for patients with chronic conditions

The primary data and analytics report users are the MiPCT physician

organizations. However, all of the payers have access to the aggregated

data, as does the University of Michigan Health System.

Looking forward, MDC is expected to add two additional multipayer

initiatives in 2016.

—Jonathan Bees



FIGURE 1 | Use of Financial Analytics Now

Click on these icons to dig deeper.

What does your organization use financial analytics for now?Q |



FIGURE 2 | Use of Financial Analytics Within Three Years


What do you expect to be using financial analytics for within three years?Q |



FIGURE 3 | Types of Finance Data Drawn On for Analytics Activity Now


Which of the following types of finance-related data does your organization draw on now for analytics activity?Q |




FIGURE 4 | Types of Finance Data Drawn On for Analytics Within Three Years

Which of the following types of finance-related data do you expect your organization to draw on for analytics activity within three years?

Q |




FIGURE 5 | Use of Clinical Analytics Now

What does your organization use clinical analytics for now?Q |




FIGURE 6 | Use of Clinical Analytics Within Three Years

What do you expect to be using clinical analytics for within three years?Q |




FIGURE 7 | Types of Patient Data Drawn On for Analytics Now

Which of the following types of patient-related data does your organization draw on now for analytics activity?Q |




FIGURE 8 | Types of Patient Data Drawn On for Analytics Within Three Years

Which of the following types of patient-related data do you expect your organization to draw on for analytics activity within three years?

Q |




FIGURE 9 | Current Applications for Working With Large/Complex Data Sets

Which of the following best describes your current applications for working with large and/or complex data sets to reveal trends or specific insights?

Q |




FIGURE 10 | Presence of Downside Risk Contracts Prompting Need for Analytics Software

Has the presence of contracts with downside risk prompted the need for or increased dependence on analytics software or services? (Among those with downside risk)

Q |



FIGURE 11 | Financial Data Analytics Capabilities

How would you describe your financial data analytics capabilities?Q |





FIGURE 12 | Clinical Data Analytics Capabilities

How would you describe your clinical data analytics capabilities?Q |




FIGURE 13 | Top Data-Related Analytics Challenges Over Next Three Years

Please select the top three data-related challenges your organization expects to face in performing analytics over the next three years.

Q |




FIGURE 14 | Top Tactical Analytics Challenges Over Next Three Years

Please select the top three tactical challenges your organization expects to face in performing analytics over the next three years.Q |




FIGURE 15 | C-Suite Title Responsible for Financial Analytics

Which C-suite title within your organization is primarily responsible for your financial analytics activities?Q |




FIGURE 16 | C-Suite Title Responsible for Clinical Analytics

Which C-suite title within your organization is primarily responsible for your clinical analytics activities?Q |



QUESTIONS FOR YOUR TEAM

meeting guideTo address healthcare analytics issues, consider asking your leadership team these questions:

1. Do we recognize that the industry’s shift to delivering value-based care and accepting risk has increased the strategic importance of analytics, and that organizations that collect data from multiple sources and study data deeply will likely be more successful clinically and financially?

2. Do we understand the extent of the industry transformation that is coming, and that the trend involves both process transformation, mainly as it relates to care delivery, and information technology transformation, a component of which is analytics related?

3. Do we recognize that the need to measure, monitor, and be compensated for value-based care requires that analysis be available on a near-real-time basis? Are we preparing to take a longitudinal view of patient care activity, so that information systems can prompt caregivers when preventive action is likely to be needed?

4. Are we establishing the right platform and systems to document our performance along value-based lines, with the objective of being competitive as payers make network inclusion and reimbursement decisions?

5. Do we recognize the importance of data and interoperability to our organization, and are we ready to take a disciplined approach to developing strategies and tactics for the future? Are we prepared to update those strategies and tactics as required, while

at the same time maintaining a high degree of commitment to the direction?

6. Do we understand the value that comes from interoperability? Are we deploying workarounds to achieve near-term benefits of examining multiple sets of data while at the same time investigating and investing in longer-term, more robust solutions to work with data from multiple sources?

7. Do we recognize that achieving further improvements on cost containment and quality outcomes will rely on supporting our actions by obtaining data from multiple sources, examining data for causal relationships, and doing so in a way that is timely enough to prompt meaningful action?

8. Are we reviewing the skillsets of our talent pool, with the idea of identifying gaps in skills and filling them? Do we see the importance of developing analytics skills in the clinical team, and are we ensuring that providers understand the importance of documentation?

9. Do we recognize the need to balance the control and flexibility that in-house software development offers with the speed of implementation that comes with acquiring third-party software and outside services?

10. Have we established funding sources for the system development, computing resources, analytics software tools, and data and content experts necessary to achieve success?



Methodology

The 2016 Analytics in Healthcare Survey was conducted by the HealthLeaders Media Intelligence Unit, powered by the HealthLeaders Media Council. It is part of a series of monthly Thought Leadership Studies. In November 2015, an online survey was sent to the HealthLeaders Media Council and select members of the HealthLeaders Media audience. A total of 350 completed surveys are included in the analysis. The margin of error for a base of 350 is +/-5.2% at the 95% confidence interval.

Each figure presented in the report contains the following segmentation data: setting, number of beds (hospitals), number of sites (health systems), net patient revenue, and region. Please note cell sizes with a base size of fewer than 25 responses should be used with caution due to data instability.

ADVISORS FOR THIS INTELLIGENCE REPORTThe following healthcare leaders graciously provided guidance and insight in the creation of this report.

Stephen Beck, MD, FACP, FHIMSSChief Medical Informatics Officer Mercy HealthCincinnati, Ohio

Indranil “Neal” Ganguly, FCHIME, FHIMSS, CHCIOVice President and Chief Information OfficerJFK HealthEdison, New Jersey

Sue Schade, MBA, FCHIME, FHIMSSChief Information OfficerUniversity of Michigan Hospitals and Health Centers Ann Arbor, Michigan

UPCOMING INTELLIGENCE REPORT TOPICS

MARCH Payer/Provider Strategies

APRIL Mergers, Acquisitions, and Partnerships

MAY Emergency Department Strategies

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Respondent Profile

Respondents represent titles from across the various functions at healthcare provider organizations.

Senior leaders | CEO, Administrator, Chief Operations Officer, Chief Medical Officer, Chief Financial Officer, Executive Dir., Partner, Board Member, Principal Owner, President, Chief of Staff, Chief Information Officer, Chief Nursing Officer, Chief Medical Information Officer

Clinical leaders | Chief of Cardiology, Chief of Neurology, Chief of Oncology, Chief of Orthopedics, Chief of Radiology, Dir. of Ambulatory Services, Dir. of Clinical Services, Dir. of Emergency Services, Dir. of Inpatient Services, Dir. of Intensive Care Services, Dir. of Nursing, Dir. of Rehabilitation Services, Service Line Director, Dir. of Surgical/Perioperative Services, Medical Director, VP Clinical Informatics, VP Clinical Quality, VP Clinical Services, VP Medical Affairs (Physician Mgmt/MD), VP Nursing

Operations leaders | Chief Compliance Officer, Chief Purchasing Officer, Asst. Administrator, Chief Counsel, Dir. of Patient Safety, Dir. of Purchasing, Dir. of Quality, Dir. of Safety, VP/Dir. Compliance, VP/Dir. Human Resources, VP/Dir. Operations/Administration, Other VP

Financial leaders | VP/Dir. Finance, HIM Director, Director of Case Management, Director of Patient Financial Services, Director of RAC, Director of Reimbursement, Director of Revenue Cycle

Marketing leaders | VP/Dir. Marketing/Sales, VP/Dir. Media Relations

Information leaders | Chief Technology Officer, VP/Dir. Technology/MIS/IT

Base = 121 (Hospitals)

Type of organization Number of beds

1–199 54%

200–499 21%

500+ 25%

Number of physicians

Base = 54 (Physician organizations)

1–9 24%

10–49 37%

50+ 39%

Region

WEST: Washington, Oregon, California,

Alaska, Hawaii, Arizona, Colorado, Idaho,

Montana, Nevada, New Mexico, Utah, Wyoming

MIDWEST: North Dakota, South Dakota,

Nebraska, Kansas, Missouri, Iowa, Minnesota,

Illinois, Indiana, Michigan, Ohio, Wisconsin

SOUTH: Texas, Oklahoma, Arkansas,

Louisiana, Mississippi, Alabama, Tennessee,

Kentucky, Florida, Georgia, South Carolina,

North Carolina, Virginia, West Virginia, D.C.,

Maryland, Delaware

NORTHEAST: Pennsylvania, New York,

New Jersey, Connecticut, Vermont, Rhode

Island, Massachusetts, New Hampshire, Maine

Title

Base = 350

51%Senior leaders

6% Marketing

leaders

0

10

20

30

40

50

60

17% Clinicalleaders

18% Operations

leaders

6% Financial leaders

35%

27%

19%

20%

Number of sites

Base = 100 (Health systems)

1–5 12%

6–20 28%

21+ 60%

Base = 350

Hospital 35%

Health system (IDN/IDS) 29%

Physician organizations 15%

Long-term care/SNF 9%

Health plan/insurer 5%

Ancillary, allied provider 4%

Government, education/academic 3%

2% Information

leaders

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Documents

PREMIUM REPORT FEBRUARY 2016 THE ANALYTICS CHALLENGE · driven by the steady advance of value-based care and the assumption of greater downside risk by providers, are trends that