By Trisha Young

Big Data and Population Health: The Power of Combining Clinical and Claims Data

Big Data and Population Health: The Power of Combining Clinical and Claims Data

As we continue to move toward a pay-for-performance healthcare model, big data as a strategic tool for population health goals to improve care, outcomes and costs remains front-and-center.

Up until recently, claims data was heavily relied upon to improve population health efforts. As a software data analytics company specializing in claims data, we know claims data has a big role to play in healthcare data analytics. However, we’re also very aware that the real power and benefit for health systems, patients and population health efforts lies in combining claims data with clinical data, namely from the electronic medical record (EMR) but lab and other data as well.

Neither clinical or claims data alone provides enough depth of information, yet they are natural complements. The power is in marrying them together for meaningful insights.

Claims data provides a retrospective look at what happened such as:

  • Prescriptions filled
  • Lab tests completed
  • Treatment costs and outcomes
  • Spans a patient’s full continuum of care

What claims data lacks, clinical data provides:

  • Important clinical details about patients
  • The process of care, which is key to improving quality of care
  • Timeliness, as it’s collected in real time

Meaningful Insights for Population Health

According to an article in Healthcare IT News, health systems seeking to turn clinical and claims data into actionable insights should consider the following strategies for population health:

  • Start with a broad data set that, at minimum, includes administrative data and clinical data. Ideally, the data will also include personal health record data, lab data, Continuity of Care Documents (CCD) data and consumer reported data such as health risk assessments and wearables data.
  • Focus on insights to better coordinate care. Make sure data is bi-directional and real-time for the creation of dynamic care plans.
  • Use the data to create a platform to drive patient engagement – a key to any successful population health effort.

Big Data Health System Collaborative

A fantastic example of big data in action can be seen In Michigan at one of the largest data-driven quality improvement efforts in surgery. The Michigan Surgical Quality Collaborative (MSQC) helps surgical teams across 73 Michigan hospitals improve patient care. The collaborative is funded by Blue Cross Blue Shield of Michigan, the state’s largest payer.

MSQC leverages 137 different types of data – including clinical and administrative data – that includes records from more than 420,000 operations performed at the MSQC participating hospitals. Operations initially focused on general surgery and now include specialty surgeries.

The data collected, analyzed and put into action has resulted in some impressive gains and programs:

  • Infections for surgical infections post-colectomy surgery dropped the more often hospital teams followed a MSQC bundle of anti-infection protocols. Costs dropped as well.
  • Data is currently being used to help patients reduce their risk of surgical complications by participating in a pre-surgery “prehabilitiation” that includes emotional factors, exercise and quitting smoking.
  • MSQC is using its data to help improve opioid painkiller prescribing practices post-surgery.

With half of U.S. medical care related to surgical procedures, MSQC is on the right track with its focus on using big data to improve surgical outcomes and overall population health.

MSQC is a great example of transforming data into actionable insights to positively drive quality, costs and patient satisfaction goals. Such an approach also positions both health systems and patients for success in the changing healthcare system, where pay-for-performance and population health outcomes will matter most.

Trisha Young is Regional Vice President of Business Development at Intellimed.


technology customer service

By Nathan Schnell

The Lost Value of Customer Service in the Tech Era

As a healthcare data analytics company, like many software companies, we automate certain processes for efficiency and scale. But, we take a completely different, more integrated approach when it comes to customer service, one we’d like to see more of among technology companies.

Automation has its upside, for sure. It allows us to get a jump on customer service issues and helps improve the quality and speed at which we deliver our service. Routine communication to our clients can be automated, customer data can be merged across systems and business processes can be integrated.

With the many benefits of automation also come challenges and potential pitfalls, including:

  • Slow or incomplete resolution
  • Customer aggravation and stress
  • Damage to your brand

Here are some ways we believe stellar customer service in the tech-driven software era can set a technology company apart.

Human-to-Human Approach: A Key Differentiator

I’m sure we can all recall a situation where we called a support phone number only to be endlessly placed on hold, transferred multiple times and then, finally, to be dropped from the call, forcing us to call back and start all over again. Or not.

Thus, the potential downfall of customer service automation. In those situations, wouldn’t it have been nice to have the option to just speak with an actual human?

At INTELLIMED, we take the approach that while automation is vital and provides many benefits to our customers, we also know it’s important to take a human-to-human perspective as well and strive to create authentic connections with our customers.

This means providing customers with the option to talk to a real live human, when needed, and to do so easily. It means reaching out to customers proactively to see how we can assist and support them, not just in the future, but today. This means making part of our company culture and everyday thinking about how we support our customers as if they were sitting in the same room with us.

In today’s tech-driven world, where it’s hard to sustain a competitive edge on technology or price, human-to-human customer service may be the only real differentiator.

Solution-Oriented Problem Solving

Even with the most top-notch products and services, things go wrong sometimes. And, it’s often how problems are dealt with that determines whether a customer will stick with you long-term or not. We like this nine-step problem-solving formula by Brian Tracy:

  • Clearly define the problem
  • Pursue alternate paths on “facts of life” and opportunities
  • Challenge the definition from all angles
  • Iteratively question the cause of the problem
  • Identify multiple possible solutions
  • Prioritize potential solutions
  • Decide
  • Assign responsibility
  • Set a measure for the solution

Compassion, Timeliness & Efficiency
You don’t often hear tech companies talk about compassionate customer service, but we think it’s important. Isn’t it refreshing (and, increasingly rare) to encounter a compassionate customer service person? If you’ve had such an experience, I bet you remember it – and the product or service – and would give that company your business again.

But it’s not enough to merely solve a problem with compassion, the challenge must be resolved efficiently and quickly. Even for situations where there is not necessarily a problem, providing efficient and timely customer service for customer questions and requests will set your company apart.

One important note about providing customer service that is efficient and timely is that it requires training and then empowering your customer service team to deal with customer issues; whereby, there is a direct link between employee empowerment and long-term customer relationships. A great book on this approach is The Nordstrom Way to Customer Service Excellence.

Understanding Customers’ Needs
Regular communication with customers helps us understand their needs so we can make updates to our data analytics software and business processes to better serves customers. Good communication also helps us manage possible risks and avoid potential problems.

Some possible ways to collect customer input include:

  • User groups
  • Online surveys
  • Phone interviews
  • Customer focus groups

Once updates and enhancements are made to software based on customer input, we can then communicate those updates to our customers, letting them know their needs were heard and addressed. 

Ongoing Product Support
Once a software sale is made, there is typically an initial customer training. Unfortunately, training and support often end there. Providing ongoing, 24/7 training support resources can help customers learn how to use the software and reap maximum benefits from it.

Ideas for ways to provide support include:

  • Frequently Asked Questions (FAQs) document
  • Online training in the form of pre-recorded webinars and web pages with step-by-step instructions
  • Monthly email with tips on how to most effectively use the software
  • Routine client check-in calls
  • And, of course, the option to call a support line and speak with a human being

Account History and Knowledge
Unfortunately, account management turnover does occur. What often doesn’t occur is a smooth transition of an account from one sales representative to another. A sign of good customer service in software sales is a sales rep that has a strong knowledge of a customer’s account history. We’ve learned that relationships often trump pricing – when a customer feels their sales rep has a good handle on their account and is a human to them rather than a number, that goes a long way in account retention.

Your Thoughts?
We’d love to hear your thoughts on how to provide stellar customer service in the age of software and automation.

Nathan Schnell serves on INTELLIMED’s leadership team as vice president of service delivery, where he focuses on providing stellar customer service to clients, expanding products and market growth.

healthcare data, preventive care

By Jennifer Zweifel

Healthcare Data and Preventive Care: A Pathway to Success in Value-Based Healthcare

Healthcare Data and Preventive Care: A Pathway to Success in Value-Based Healthcare

An area where healthcare data is beginning to make inroads and has tremendous potential for significant impact is in preventive healthcare.

Preventive care plays in important role in value-based healthcare – the direction healthcare models continue to evolve into as they move away from fee-for-service. A couple notable factors act as incentives for placing prevention at the forefront of care: 

Changing Hospital Revenue Mix
Hospitals face changing payment mixes due to:

  • High volume of Medicare usage by an aging population, notably the Baby Boomers.
  • Increased numbers of Medicaid patients, in part, a result of the Affordable Care Act (ACA) Medicaid expansion.
  • Decreasing reimbursements from commercial payers.

With payments for quality on the line, hospitals have a strong incentive to keep these populations on the preventive continuum of care, especially those most at risk for costly chronic health conditions like diabetes. 

Hospital Readmissions
There’s no doubt that hospitals with successful readmissions rates have leveraged big data to do so. This requires using data for proactive patient care, including using data platforms that can detect harmful drug side effects and interactions so pharmacists can assist doctors in tailoring a patient’s drug regimen to optimize its therapeutic effect while mitigating risk.

Feeling the Squeeze
Given changing revenue mixes, managing hospital readmissions and much more, it’s a rare hospital that’s not feeling the squeeze these days. Let’s look closer at how data can support preventive care, thus helping fuel success in a value-based model.

Before we jump in, we want to acknowledge what we all know and are watching unfold – it’s hard to say what will happen with ACA in the current political climate. If ACA is repealed, it’s also unclear what will emerge in its place. It does remain reasonable, though, to assume that a payment for outcomes versus payment for service model will likely continue to shape the healthcare industry.   

Quality Measures
For years, providers have submitted quality measures for programs such as Hospital Inpatient Quality Reporting (IQR), Hospital Outpatient Quality Reporting (OQR) and Physician Quality Reporting System (PQRS). Now these measures are tied to penalties and incentives. There is an opportunity here to leverage these quality and performance measures at a population health level – recognizing trends in vulnerable populations – for prevention. This will, however, require data tools in the form of sophisticated data analytics and data warehousing.

Precision Health
At a more granular level beyond broader population health, integrating healthcare data across data silos can allow hospitals to be more predictive and capture similarities across defined patient populations and at the individual level for more personalized risk profiling. The idea of precision health is the ability to narrow down to very specific segments of a population. Being able to predict risk at this more granular level will allow for proactive preventive treatment and improved cost management.

Claims Data and Preventive Healthcare
While we are in the business of providing healthcare claims data analytics, we recognize that claims data is a piece of the larger data puzzle for preventive healthcare. Integration of various forms of data for business intelligence and improved care may be the only way hospitals can weather shifting payment mixes, care models, penalties and more.

With Medicare setting clear goals for 90 percent of all payments to be tied to quality or value by 2018, healthcare data integration is a must to achieve this goal and bend the cost curve. Cloud technology and warehouse-based big data and analytics platforms are an investment for a hospital system. But without them, there will simply be no way to gain the insights hospitals need for success in preventive care or more broadly across their care outcome and business goals.

Jennifer Zweifel serves on INTELLIMED’s leadership team as chief financial officer, a role she has held for 21 years. Jennifer is an Arizona State Graduate. In her spare time, she enjoys cooking, reading, dancing and travel.

healthcare big data

By Bill Goodwin

The Outlook for Healthcare Big Data in 2017: Integrating Data for Business Decision Making

The Outlook for Healthcare Big Data in 2017: Integrating Data for Business Decision Making

Big data has held big promise for healthcare for some time now. However, healthcare leaders continue to struggle with fully integrating healthcare big data into clinical and business decision making in ways that positively impact patient care and their bottom lines.

So, what does 2017 hold for healthcare big data and using big data in hospitals? We don’t have a crystal ball, of course, but given what we’ve seen in our work in healthcare data analytics, in our conversations with our clients who work with data regularly and in keeping up on the latest predictions, here are some thoughts.

Closing Data Silos
I recently read a blog by Forrester’s Kate McCarthy where, not surprisingly, she predicts in 2017 that payers and providers will need to integrate unstructured data to gain patient and customer insight. She notes health clouds as a tool for providers to utilize.

We see the challenge of integrating huge amounts of healthcare big data from doctors’ offices, hospitals, labs, clinics and claims data regularly. Of course, the nation’s large healthcare systems have already created internal health information exchanges for their own data to provide access to one set of records across the healthcare system. Examples include Kaiser Permanente’s Health Connect and Pittsburgh Health’s Data Alliance. Integrating data across systems outside of their own and integrating clinical data with business intelligence data will be the next big step for many larger healthcare systems in 2017, while others will remain focused on connecting their internal data silos.

Data and Consumer Marketing
In 2015, the percent of insurance business served by group contracts dropped by 48 percent, an indicator that healthcare payers and providers will likely continue shifting to other models including increased growth in business-to-consumer (B2C) models. Insights from big data can be used to tailor and personalize patient experiences within the healthcare system. Healthcare will continue to implement strategies informed by data from the world of B2C marketing.

Data & Clinical Care
Obamacare outcome mandates around things like hospital readmissions have prompted healthcare systems to leverage healthcare big data to tackle the challenges that impact healthcare costs and quality the most. 2017 will see an increased use of big data for prevention, diagnosis, treatment and follow-up care. Big data will also play a major role in personalized medicine as a treatment option.

Patient-Collected Data

It is predicted that by 2020, 40 percent of employees can cut their healthcare costs by wearing a fitness tracker. As the technology of wearables evolves, integration of patient-generated and collected data will begin to play a greater role in employee benefits and insurance plans as well as informing clinical care. On the heels of fitness trackers like Fitbit, Jawbone and others, now come the next wave of devices focused on chronic diseases like diabetes and heart disease. These tools offer more promise for integrating patient-collected data with clinical data for monitoring and delivering care.

Evolving Role of the CIO
We don’t envy the role of the hospital CIO, as it’s a challenging job in today’s complex healthcare landscape. The role of healthcare CIO will continue to be one of working cross-functionally within the healthcare organization. CIOs will need to continue to challenge how things have always been done and use data in new and innovative ways in the rapidly transforming healthcare market.

As we head into a New Year, we look forward to seeing how big data will evolve healthcare. Regardless of what happens, we know big data will continue to play an increasing role in the delivery of care and in the business decisions that are made within healthcare organizations.

We’d love to hear your thoughts on how big data will impact healthcare in 2017. Please share your comments!

Bill Goodwin, MBA is CEO of INTELLIMED, a healthcare data analytics company based in Phoenix, Arizona.

healthcare claims data, holiday accidents

By Admin

Healthcare Claims Data & Holiday Accidents [infographic]

Healthcare Claims Data & Holiday Accidents [infographic]

According to healthcare claims data, holiday accidents in the form of more ladder falls do indeed occur in the months of November and December during the holiday season. This results in more emergency room visits related to ladder falls. Our infographic has you covered with safety tips to avoid ladder falls this holiday season and still enjoy hanging your holiday cheer.

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.



Big data and healthcare.

By Ed Willard

Why Utilizing Insurance Claims Data is Necessary for Any Healthcare Strategy Team

Why Utilizing Insurance Claims Data is Necessary for Any Healthcare Strategy Team

Using insurance claims data for strategic healthcare decision making and understanding market dynamics is relatively new to the healthcare market, and it is becoming a necessary part of any strategic planning process. While using claims data in this way can be very valuable, there are some principles to keep in mind to ensure you obtain the most benefit from the data (and avoid the mistakes many organizations have made when pursuing claims data).

How Do You Ensure the Claims Data You Acquire is Actionable?  
Only robust, transparent and detailed claims data is valuable in organizational strategy. The following factors are critical with regard to claims data:

  • Coverage: When exploring non-institutional claims data, the most important factor to consider is whether the data has enough market coverage – at a minimum, it will have 65 to 85 percent coverage. Without at least this level of coverage, you won’t be able to get a holistic view of the market, understand your competitors’ activities or use the data to analyze market dynamics.
  • Transparency: A lack of comparable, transparent healthcare data is an ongoing obstacle for most organizations and extends to claims data as well. When it comes to transparency, claims data should be cleaned and updated frequently as well as managed for duplicates. Additionally, stay away from data providers that don’t offer transparency in types of insurance companies, shared patients, etc. The more transparent the data, the more accurately you can understand the market and, in turn, craft stronger strategic objectives and action plans.
  • Detail: The level of detail in claims data is very important as well, notably for more complex decisions such as increasing market share among specific insurance companies or understanding physician outpatient activity by procedure, by specific payer and by location.

Is Claims Data Alone Enough?

While claims data is a critical part of data-driven decision making, we at INTELLIMED, a healthcare data analytics company, are the first to acknowledge that claims data alone will not provide all of the data needs for strategic decision making or deliver a full picture of the healthcare ecosystem of a city, state or region. Claims data definitely offers a large portion of what is needed, but not all.

By combining claims data, available state discharge data, and demographic data with a healthcare organization’s own data — including information from its electronic medical record (EMR) — claims data can be used to understand what is happening within an organization and within the external environment. The EMR in particular, with its rich information around patient encounters and clinical data, can yield a more detailed view of a patient’s progress through the encounter and his or her status at discharge, while the claims data will provide a holistic view of the patient’s interaction with the healthcare system.

What Can Be Done with Claims Data?

Every healthcare encounter creates a claim for payment from physicians, hospitals, pharmacies and other healthcare providers. There are two ways that claims are submitted and the data collected:

  • UB-04 is the standard billing form used by institutional providers for claim billing. Although it was developed by the Centers for Medicare and Medicaid (CMS), it has become the standard form used by all insurance carriers.
  • CMS-1500 insurance claim form is used for fast professional health care claims submission. The CMS-1500 form is the standard claim form used by a non-institutional providers or suppliers to bill Medicare and commercial carriers. Durable medical equipment providers also use this form to bill regional carriers.

Among the more common uses of external claims data is accessing outpatient market data to understand the connections between doctors, patients and payers beyond the inpatient setting. Other purposes include utilizing the data for physician relations and marketing, including increasing physician market share, facility loyalty and other physician patterns. Using data strategically for both patient- and physician-focused marketing campaigns can yield a positive return on investment.

Additionally, claims data can be mined for important information that has an impact on decisions in many areas, including competitors; service line expansions, decreases or closures; purchase of independent physician practices and clinics; and marketing and pricing strategy, including:

  • Which hospitals have the highest and lowest prices by service line.
  • How far consumers travel for services.
  • Which health plans provide the best discounts and pay the highest by service.
  • Emergency department and outpatient usage among commercial and non-commercial consumers.
  • Utilization patterns of the commercial and non-commercial population.
  • Payer mix by geography, specialty, and procedure, among other factors.

While all healthcare organizations have access to their own internal claims data, there is no publicly available source for competitor claims data, therefore it is essential organizations find trusted partners who have extensive claims data to support strategic decision making. Internal data, along with state discharge data, simply is not enough in today’s increasingly competitive marketplace

A New Data Paradigm
The changes in the healthcare system at all levels triggered by the Affordable Care Act (ACA) have put new emphasis on using claims data to facilitate cost savings at a system-level and for aligning with value-based purchasing initiatives. Claims data can also help to determine whether established clinical and quality safety guidelines are being met. In addition, to achieve the three goals of population health management and analytics: improved outcomes, increased patient safety, and decreased costs – which many organizations have prioritized – combining claims data with clinical data is absolutely essential.

ACA, coupled with the trend of an increasingly active healthcare consumer, has shifted the way healthcare organizations view market share. In fact, developing market share has drastically changed in the last few years. No longer are the days of a volume-based approach focused solely on patients in beds and emergency department usage.

The focus on delivering patient-centered care – one of the “Aims for Improvement” in the Institute of Medicine’s 2001 report, Crossing the Quality Chasm: A New Health System for the 21st Century – is shaping a new paradigm around market share and using data for strategic decision making. Factors such a facility convenience, online reputation, facility and physician ratings, and other variables are all influencing consumer choice. Organizations must market as much to the consumer as to the physician. Many healthcare organizations are looking to the best practices of retail marketing to reshape their consumer interactions.

Using data – including robust, transparent and detailed claims data – will allow healthcare organizations to be aware of the elements that have an impact on their market, a critical factor in organizational strategy and decision making. Such an approach will allow healthcare organizations to evolve with the new landscape and set the course for where they wish to be in the near as well as more distant future.

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.

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.

Big Data and Population Health: The Power of Combining Clinical and Claims Data
technology customer service
The Lost Value of Customer Service in the Tech Era
healthcare data, preventive care
Healthcare Data and Preventive Care: A Pathway to Success in Value-Based Healthcare
preventive care, healthcare data
Healthcare Data Silos: From Medical Tragedy to Opportunity of Accelerating Returns
healthcare big data
The Outlook for Healthcare Big Data in 2017: Integrating Data for Business Decision Making
healthcare claims data, holiday accidents
Healthcare Claims Data & Holiday Accidents [infographic]
Healthcare data analytics 3.0
Actionable, Trusted and Contextual Data Key to Healthcare Data Analytics
Understanding Healthcare Market Share Changes in a Value-Based, Patient-Centered Landscape
Understanding Healthcare Market Share Changes in a Value-Based, Patient-Centered Landscape
Big data and healthcare.
Why Utilizing Insurance Claims Data is Necessary for Any Healthcare Strategy Team
Healthcare big data
Three Tips to Prevent Big Data From Causing Big Problems