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healthcare, healthcare IT, HIT, wearables, digital health

By Nathan Schnell

4 Healthcare IT Trends to Watch in 2018

4 Healthcare IT Trends to Watch in 2018

As we head full steam into another year sure to be full of change for healthcare, we thought we’d offer a roundup of healthcare IT trends predicted for 2018 by health IT writers, editors and analysts. Ready? Here we go…

Artificial Intelligence

While artificial intelligence (AI) is currently used to automate simple tasks, 2018 is predicted to be the year where it will make its way into clinical support and decision making. Currently many healthcare organizations already use AI for clinical decision support, population health, disease management, readmission and claims processing. But experts believe 2018 will be the year AI will make inroads into cancer diagnostics, pathology and image recognition, according to a recent SearchHealthIT article.

Health Data Management predicts that by 2021, 20 percent of healthcare and 40 percent of life science organizations will have recognized a 15 to 20 percent in productivity gains by adopting AI technology, noting that adoption resides mostly in large academic medical centers at present. Industry analyst Forrester predicts that AI as well as the Internet of Things (IoT) will be part of the disruption of siloed healthcare ecosystems in 2018.

Digital Health

According to seed fund Rock Health, a record-breaking $3.5 billion was invested in 188 digital health companies in the first half of 2017, with the number of wearables is set to hit 34 million by 2022.

Digital health has been gaining momentum for many years with the wearable trend. According to a Forbes article, the most frequent users of wearables are the least likely to be hospitalized.

Additionally, the Food and Drug Administration (FDA) recently issued new guidelines that loosen regulations for some mobile health technologies, recognizing that clinical evidence supports better health outcomes with mobile device usage. This change will likely encourage healthcare organizations to better embrace the integration of consumer digital health device data.

Telehealth and telemedicine are predicted to grow as more states update laws to expand access to these services. With one in five U.S. adults suffering from mental illness, a noteworthy predicted area of expansion is telemental and telebehavioral health services, according an article by SearchHealthIT.

Blockchain

The promise of blockchain, the technology invented to power Bitcoin, has been around since 2008. However, this year may be the year its value starts to be recognized and leveraged within healthcare. HealthDataManagement predicts that by 2020, 20 percent of healthcare organizations will be using blockchain for operations management and patient identity.

However, as noted by SearchHealthIT, blockchain has “yet to prove itself in the demanding crucible of health IT systems and clinical healthcare settings,” but notes that “IBM, Intel, Google, Microsoft  and others have units dedicated to development of blockchain products, including for healthcare.” Federal health IT officials are promoting it heavily as well.

Electronic Health Record Analytics

To be successful, EHRs will need to move into providing analytics that support population health initiatives and value-based healthcare – and many predict 2018 will be the year where headway will be made by EHRs in analytics. The big players like Cerner and Epic already have population health products and other smaller vendors like cloud-based AthenaHealth do as well. More are predicted to join and more healthcare organizations will likely take advantage of these products.

Nathan Schnell is Vice President of Service Delivery at Intellimed. 

By Kim Carlson

8 Ways Claims Data Supports Population Health

8 Ways Claims Data Supports Population Health

Effective population health initiatives implemented by hospitals, large physician groups, payers, self-funded employers, among others require data analytics to be successful. The right data can inform population health strategy, goals and outcomes. While healthcare claims data is not the only data required for population health, it is a big factor in driving improvements in population health programs.

Here are eight ways we believe healthcare claims data can inform population health initiatives:

  1. Managing Overall Costs: Claims data can shed light on the disparate prices doctors and hospitals charge for the same procedures. The data can show total spending within an institution by procedure as well. Claims data can reveal which service lines are performing well and which are struggling with cost-containment.
  2. Physician Performance: Claims data can help to determine the performance of individual physicians through analysis of the services provided by diagnostic code. Data can reveal if physicians are following nationally recognized medical protocols. An example is diabetes care: According to the Pew Charitable Trusts, claims data can reveal whether a doctor followed nationally recommended protocols for treating patients diagnosed with diabetes. How many received quarterly exams? Did they receive an eye exam? How many were admitted to a hospital?
  3. Empowered Consumers: Some states through all-payer claims databases (APCDs) are making claims data available to healthcare consumers, with the idea that when consumers can compare prices across physicians and hospitals, they will make better and more informed decisions regarding both quality and cost.
  4. Improving Quality and Outcomes: When combined with clinical data, healthcare claims data can provide a very broad view at both the patient-level and population-level of interactions across the continuum of care within a healthcare system.
  5. Reduce Hospital Readmissions: Claims data can help to reduce costly hospital readmissions by uncovering areas by service line and/or at the physician-level where readmissions are occurring most frequently.
  6. Patient Engagement: Patient engagement is a key to successful population health. Claims data can help reveal when to reach out to patients as well as whether patients are filling prescriptions or following-up with recommended lab tests. In the past, technology lagged when using claims data to reveal patient patterns. However, newer analytics allow for as little as 15 days to reveal patterns such a prescription refills or follow-up tests, providing healthcare clinical teams a reasonable window to follow-up with patient outreach.
  7. Strengthen Coordination of Care: Claims data, notably when coupled with clinical data, can inform the actions of care teams that can include physicians, care managers, health coaches, caregivers and even the patients. Creating data transparency through patient portals and other tools that aggregate data into usable information allows for care plans to be adjusted to the patient’s needs.
  8. Amp Up Reporting: The best reporting reveals where there are opportunities to improve and where health systems have effectively made changes. Claims data when coupled with clinical and other data can reveal these insights. Such insights can improve population health initiatives that help to contains costs and improve healthcare quality resulting in healthier populations and healthcare systems.
physician leakage, outward migration, healthcare data analytics, big data healthcare

By Ed Willard

Physician Leakage & Using Data to Prevent Outward Migration

Physician Leakage & Using Data to Prevent Outward Migration

Physician referrals are a link between primary and specialty care and are vital to patient management and volume within a healthcare system. In fact, visits to specialists constitute more than half of outpatient physician visits in the United States. Physician leakage refers to the process of physicians referring patients to competing hospitals or providers outside of their network.

A recent article Dropping the Baton: Specialty Referrals in the United States notes the following breakdowns and inefficiencies in all components of the specialty-referral process:

Outward migration affects both patient care and a hospital’s bottom line:

  • Reduced continuity of care
  • Delays in diagnosis and treatment
  • Duplication of services and testing wasting hospital resources
  • The simultaneous use of multiple drugs to treat a single ailment or condition, or polypharmacy
  • Increased risk of malpractice lawsuits
  • Weakened physician-patient relationships

Physician Leakage and Outward Migration

Looking more closely at the financial impact of outward migration, let’s look at this example provided by Lance Fusacchia in an HFMA article:

“Consider for a moment the potential financial losses of referral no-shows in terms of actual dollars. As an example, a typical healthcare system with 200 providers, each serving a panel of 2,000 patients. Of those 400,000 patients, it is fair to estimate that 50 percent visit their physicians and 30 percent of those visits result in a referral. That makes 60,000 potential referral visits. If 30 percent of those referrals don’t happen (the average number of no-shows, as cited previously), that’s approximately 18,000 lost referrals. According to findings in one study, a single no-show costs a provider, on average, $210. Multiplying that amount by 18,000 no-shows results in $3.78 million in lost revenue. If a health system could avert even 25 percent of those lost referrals, it could recover nearly $1 million in lost revenue.”  

The Role of Data in Preventing Outward Migration

Data plays a major role in the prevention of outward migration. Having data alone, however, won’t solve the challenges. Being able to have the data analytics tools to gain key insights from the data will provide the needed information to adjust physician referral management programs and processes.

  • Comparing Past to Present Data – Historical data can allow for a view of events that may be factoring into lost business. A referral drop is a cause for concern to be investigated and resolved.
  • High-Tail & Long-Tail – These are common terms in marketing and should be applied to outward migration data analytics. Basically, high-tail means that 80% of monitored events occur in the first 20% of a population metric. Low-tail comprises the remaining 20% of monitored events, but it can often outweigh the overall high-tail impact. By analyzing where business is coming from on both ends of the tail, you may be surprised that the long-tail is equally, if not more, responsible for driving volume.
  • Where You Stack Up in the Industry – Data analytics can show you where you stack up with your competitors, helping you to establish a baseline to measure performance against.
  • Interoperability – One of the holy grails of healthcare is interoperability both within and outside of a healthcare network. Healthcare systems have long operated private health information exchanges within their networks and the Affordable Care Act has helped to promote public exchanges to share data across systems. The continued advancement of this data sharing effort will progressively close the referral tracking information gap that challenges both physicians and hospital executives.

At Intellimed, we have provided healthcare data analytics solutions to the U.S. hospital marketing for 30 years. Contact us to learn more about leveraging data to prevent outward migration and stop physician leakage.

Ed Willard serves as INTELLIMED’s Executive Director of Business Development and is a member of the INTELLIMED leadership team. In his free time, he enjoys soccer and is involved in several local soccer organizations.

precision health, population health management, healthcare data analytics

By Kim Carlson

Precision Health Will Require Both High Touch and High Tech

Precision Health Will Require Both High Touch and High Tech

Stanford Medicine Precision Health has a bold vision for medicine:

Over the past century, the focus in medicine – and academic medicine – has been on the diagnosis and treatment of acute diseases. Although shining our brightest light on treating the most complex conditions has resulted in many medical advances, patient care has often been fragmented and has lacked specificity.

We have within our grasp the ability to completely change this approach. From cancer to cardiac diseases, from neurological diseases to inborn errors of metabolism in infants, from food allergies to heart transplantation – our advances in diagnostic methodologies and therapies will lead to the most precise molecular diagnoses and to treatments that are individually tailored based upon these diagnoses.

So how do we move toward this bold vision for medicine? What are the critical steps to make precision health a reality?

Precision health will require both high touch and high tech.

High Touch: Patient-Centered Care

Many believe that true precision health is not about tailored therapies to cure diseases once they have occurred, but rather to prevent them from occurring in the first place. This focus requires radical patient-centered care and a high touch approach that includes:

  • Reviving the patient-provider relationship model from a bygone era, where the connection and communication between the patient and doctor was a critical aspect of care and healing.
  • Changing the way hospitals are designed and run to better accommodate patients’ needs and create a healing supportive environment.
  • Accommodating patients’ desires to be engaged in their care and customizing care to patients’ needs, values and choices.
  • The inclusion of family and friends as part of the care team.
  • Freely sharing information among the care team, including providers, patients, caregivers and care partners.
  • Acknowledging and leveraging the role the Internet plays in changing the way patients learn about and engage with health and medical information.

High Tech: Data & Genomics

Initiatives like the $130 million Precision Medicine Initiative Cohort Program – part of former President Obama’s precision health initiative being conducted by the National Institute of Health – are changing the way data is collected and used to advance the goals of precision health.

The goal of the initiative is to study the health records of more than 1 million people to learn which individuals respond to certain types of drugs, are at risk for a certain disease, maintain health and fitness, age and die. The anonymous data from all 1 million individuals will be made available to any interested researcher who wants to study one of the largest medical research cohorts ever.

Genomic data combined with other forms of data, including clinical and claims data, will play a major role in precision health. An executive report by IBM entitled Precision Health and Wellness: The Next Step for Population Health Management states, “from an analysis of the projected state of healthcare by 2020, we believe that population health management will converge with precision medicine, which incorporates genomic data to personalize optimal treatments for individual patients, to create an entirely new paradigm in healthcare services.”

In fact, 60% of respondents surveyed for the IBM report said that genomic data tops their needs by 2020, underscoring this predicted intersection between population and precision health.

Leveraging multiple data sources to create smart data that can deliver personalized care to patients will require new collaborations which we are already seeing form between physicians, engineers, computer scientists and business leaders, among others.

Additionally, the continued advancement of private and public health information exchanges will continue to bring together patient data from disparate sources to provide a more holistic view of the patient.

Next Steps

I’m sure we can all agree that healthcare is undergoing a radical transformation. It’s impossible to say where we’ll end up, but it’s clear that healthcare data will be part of our journey. Bringing the patient back into the care model will also be critical if we are to realize the promise of precision health, as data alone will not get us there.

Kim Carlson is Regional Vice President of Business Development at INTELLIMED, a healthcare data analytics company. 

Understanding Healthcare Market Share Changes in a Value-Based, Patient-Centered Landscape

By Gene Koch

Understanding Healthcare Market Share Changes in a Value-Based, Patient-Centered Landscape

Understanding Healthcare Market Share Changes in a Value-Based, Patient-Centered Landscape

The old model of hospital/healthcare market share that focused on high-margin, high-volume procedures (notably inpatient) used to be the best way to evaluate a healthcare facility’s competitive position. However, this model is quickly becoming less relevant as a new healthcare model – largely fueled by the Affordable Care Act – is taking hold. The new model focuses on transforming the healthcare system from an inpatient sick care model to an outpatient model centered around community-based healthcare that values:

  • Quality of care over volume of care.
  • Operational efficiencies to deliver the highest quality care at the best cost.
  • Placing the healthcare consumer/patient at the center of care and delivery.

Before we dive into how these changes affect market share and how data can be leveraged for strategic planning to increase and improve market share, let’s look at some compelling data from the American Hospital Association’s 2015 environmental scan that will continue to impact market share changes:

  • 78 million baby boomers are expected to live longer, and, for many, with chronic conditions that will continue to put pressure on the healthcare system.
  • The percentage of workers with high-deductible plans increased from four percent in 2006 to 20 percent in 2013 – and is projected to continue rising.
  • A decline in the number of uninsured individuals as a result of health care reform will reduce bad debt for healthcare institutions, but out-of-pocket increases for the consumer will likely keep volume weak.
  • Payers are adapting to affordability imperatives by actively excluding some hospitals whose costs are higher and collaborating with those institutions willing and able to accept lower reimbursement rates.
  • The economic feasibility of independent medical practices will continue to evaporate, with an estimated 75 percent of physicians likely to become hospitalists by the end of this decade.
  • Seventy percent of organizations that reported a transition toward value-based contracts by payers also saw an increase in healthcare consumerism, with patients seeking greater price transparency, challenging orders for services and negotiating payments.

Furthermore, we know that a decline in inpatient care – driven by technological advances in medicine, economic considerations and the ACA – is pushing both horizontal consolidation (hospitals merging with other hospitals) and vertical consolidation (hospitals consolidating with other healthcare provider entities) across all U.S. health regions, according to a Journal of the American Medical Association article.

Healthcare Data and Market Share Changes

The most important part of a healthcare organization’s operational strategy is its ability to keep up with the ever-changing healthcare landscape by being aware of all elements that impact its market. This is where data – both internal data and external data such as healthcare claims data – can be of great value. Let’s take a look at three key areas where hospitals typically seek to gain market share and how the right data will support better strategic decisions with the results being increased market share.

Healthcare Data & Patients
Patient loyalty is critical in the new healthcare model. The ability to measure your healthcare consumers’ experiences across their entire healthcare network is more important than measuring solely on a single point of care. Data can show you where consumers are choosing to go for their care by zip code as well, so that changes and trends can be pinpointed for all data points in a data set.

Healthcare Data & Physicians
The new model of healthcare is focused on creating a healthcare system that is integrated and works with its physician partners to meet the needs of patients across the continuum of care. Data can help you monitor, measure and assess the strength of your facility’s physician network, including both primary care doctors (key components of the new accountable healthcare models) and specialists.

Healthcare Data & Payers
External data such as claims data can help you to determine the payer mix among your competitors. It is also possible to determine which healthcare system or hospital is getting the best reimbursement for procedures among payers in the market. In order to obtain this level of detail, you’ll want to ensure that the is robust enough and covers at least 65-85 percent of the market.

Gene Koch serves as INTELLIMED’s Chief Operating Officer and is a member of the INTELLIMED leadership team. In his free time, he loves to play golf, travel for pleasure and mentor students in several MBA business classes.

 

 

Healthcare big data

By Bill Goodwin

Three Tips to Prevent Big Data From Causing Big Problems

Three Tips to Prevent Big Data From Causing Big Problems

If you are like most leaders in business, you hear the words “Big Data” being used in promotions, internal meetings, vendor presentations and more. Big data – to an increasing extent – has become synonymous with “we can help you find the answers you need and improve profits.”

And, there is some truth in this statement. According to the International Institute for Analytics, businesses that use data will see $430 billion in productivity benefits over their competition not using data. Forrester predicts that real-time streaming insights into big data will be the hallmarks of data winners going forward. Without a doubt, data can help us make better more informed decisions. However, it is possible to over-rely on big data as a panacea for answers to complex healthcare business decisions.

Countless times I have been in meetings with vendors, internal personnel and clients where healthcare big data is mentioned in some form or another as being the solution to helping (better yet telling) them what to do. The competitive pressure in all markets today forces individuals to make decisions faster and more accurately, so the appeal of fresh insights from new clinical data analysis becomes extremely appealing.

In many ways, tapping into healthcare big data analytics can help, but all of us should be extremely careful about placing too much stock in there always being clear, action-oriented and effective go-forward strategies to be found in big data. In fact, I have been in front of many prospective clients over the last few years and they mention, albeit sometimes reluctantly, that they have been burned by previous companies who offer data-driven tools designed to provide answers they had previously been unable to find. So not only has big data in hospitals been marketed as the solution, it has also started to develop a reputation as being overrated.

Big data, more accurately described, is a general, all-inclusive term for a variety of complex data collection, processing and analysis generation that traditional applications are unable to handle. There is no question the accumulation and analysis of new data can be helpful to every organization. However, take ten organizations in the same industry that have the same big data inputs and I guarantee all will come out with different conclusions on what they should do next. Seems logical, yes, but how do you ensure your organization is not one of the ones that makes a critical misstep?

While big data can certainly provide critical insights for healthcare decision makers, we must approach big data cautiously and through a measured perspective. Here are three important considerations with regard to big data to leverage immediately:

  • Be Aware of Personal and Internal Bias
    All analyses have some level of personal or organizational bias. Whether it is how the analyses are set up or how the information is interpreted, it is essential to a) know there will always be bias and b) try to factor that out in the final interpretation. In other words, while there is no way to fully eliminate bias, you can be aware of what it is and modify your interpretation accordingly. Most leaders are aware that there is bias in all analyses, yet some continue to make decisions without taking bias into consideration.
  • Understand Correlation Versus Causation
    I have seen too many leaders make detrimental decisions when they mistake correlation with causation. High positive or high negative correlation does not always mean causation, whether one is looking at two variables or a multi-variable analysis. Be sure to factor this in before any major decisions are made. Dig deeper, look at the problem or opportunity from more angles and get other viewpoints before settling in on the final decision.
  • Tap Into Your Intuition
    In this new data-driven society, we are becoming so data dependent that using intuition is becoming a thing of the past. In fact, I would argue the art of intuition has been lost in many organizations. We have all had situations where the “data” told us to turn left, but our gut told us to turn right. And, how many times did all of us turn left due to an analyses only to find out right was the better direction? Intuition is essential to any decision-making process and simply cannot be excluded. If you don’t have a good feel for the decision you need to make, rely on data more. If your intuition is telling you what decision you should make, pay more attention to it, regardless of what the analysis suggests.

Some data experts predict that we have already begun to move away from the era of big data in favor of “fast data” and “actionable data,” noting that most businesses don’t use a fraction of the data they have access to and should focus on asking the right questions to make the best use of data, big or otherwise. Certainly these data analytics changes will enable us to continue to enhance our insights and subsequent decisions.

With the continual advancement of how we access and analyze big data, it’s hard to argue that it’s not a necessary component of healthcare decision making – but it’s not the only factor. Regardless of the direction that big data goes, coupling the knowledge gained from data with our experience and intuition – and knowing when to favor one over the other – will become increasingly important in our complex healthcare landscape.

Bill Goodwin is CEO of INTELLIMED, a leading healthcare analytics company.

healthcare, healthcare IT, HIT, wearables, digital health
4 Healthcare IT Trends to Watch in 2018
8 Ways Claims Data Supports Population Health
physician leakage, outward migration, healthcare data analytics, big data healthcare
Physician Leakage & Using Data to Prevent Outward Migration
precision health, population health management, healthcare data analytics
Precision Health Will Require Both High Touch and High Tech
preventive care, healthcare data
Healthcare Data Silos: From Medical Tragedy to Opportunity of Accelerating Returns
Understanding Healthcare Market Share Changes in a Value-Based, Patient-Centered Landscape
Understanding Healthcare Market Share Changes in a Value-Based, Patient-Centered Landscape
Healthcare big data
Three Tips to Prevent Big Data From Causing Big Problems