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Using Business Intelligence to Promote EHR Adoption

Published: December 18, 2007

Electronic health records (EHRs) have an immense potential as a rich source of analytical data for clinical, financial, operational and research purposes.

The value of the electronic health record (EHR) has been taking a beating in the press lately. A sampling of news stories chronicling the difficulty of successfully getting a return on investment from EHR adoption include:

• The top five reasons for EHR failure,
  
  
• A study on the rise in failure and de-installation of EHRs,
  
  
• The unlikelihood of EHRs to reduce health costs,
  
  
• How EHRs fail to live up to the promise of improving healthcare quality.

Perhaps the greatest challenge to the value of EHRs came from the Congressional Budget Office (CBO) with the release of their report on long-term healthcare spending. Dr. Peter Orszag, the CBO’s director, stated that the estimates of potential savings through implementation of EHRs do not align with the agency’s data and are likely to be substantially less than people think.

What these and other reports seem to be overlooking is the immense potential of the EHR as a rich source of analytical data for clinical, financial, operational and research purposes. In short, they are overlooking the business intelligence value of the EHR. Where analytics are mentioned, it is pretty much the equivalent of a footnote.

The twist is that while seemingly dampening the enthusiasm for EHRs as operational applications, these same reports are practically mandates for business intelligence in healthcare. This is especially true of the 35-page analysis The Long-Term Outlook for Health Care Spending from the CBO, with its emphasis on the concept of “comparative effectiveness” as a solution to spending healthcare dollars wisely.

But let’s drill down on this a bit to see the various ways that EHRs can improve quality, efficiency and effectiveness of care.

What is the Electronic Health Record?

EHRs are electronic versions of the paper records on a particular patient. They combine information from a person’s EMR (electronic medical record) and their PHR (personal health record). The former is typically managed by the care provider (usually the primary care physician), while the latter is typically managed by the patient him/herself.

When combined, these two types of records give a comprehensive profile of the person’s health status and healthcare history. At least this is the case when both are consolidated from the various sources of information that are needed to provide this profile. But as we all know, not all of the sources of information keep it in electronic form, nor do all of the sources report into the keeper of the EHR. In some cases (more often than any of us realize, I suspect), the various sources of data on a patient’s health services may not even know the others exist.

The potential risk to a person and their health from this fragmentation of data is high. And the promise of improved safety, efficiency, efficacy and reduced duplication of services seems intuitively high as well. These are the reasons that the EHR has made it onto the political platforms of presidential candidates, and onto the agendas of senators, congressmen and state governors. And these are the driving forces behind numerous healthcare quality and safety agencies and groups.

Common Components of Electronic Health Records

For our purposes, we need to focus on ten key EHR components:

• Patient demographic information: Age, gender, location, etc.
  
  
• Health history: Medical diagnoses, examinations and health/illness progress reports.
  
  
• Medication history: Medicines taken, allergies, side-effects, interactions, immunizations.
  
  
• Laboratory test results: Diagnostic labs performed and the resulting measures.
  
  
• Radiology images: X-rays, CTs, MRIs, etc.
  
  
• Clinical, endoscopic, laparoscopic or other clinical photographs.
  
  
• Medical and healthcare recommendations for specific medical conditions and/or special care needs.
  
  
• Appointment records, reminders, persistency/compliance logs.
  
  
• Financial information: Billing records and insurance information.
  
  
• Legal information: Advanced directives, living wills, and health powers of attorney, etc.

Not all EHRs contain all of this information, but when they do, it is easy to see that this is a rich source of information about a patient with a broad range of perspectives. Just getting all of this in one place – let alone in a consolidated, electronic form – is a monumental task for even the most organized individual or organization. For instance, when my mother-in-law underwent a highly risky surgery a few years ago, my wife spent one of the worst days of her life becoming the consolidator of legal, financial, insurance, governmental, personal, medical and medication information. And this was under extreme emotional distress. She spent the whole day on the phone while at the hospital with her mother coordinating doctors, nurses, lawyers and other experts, while I was finding documents at home.

In addition to having a varied list of requirements, the EHR is going to be a large record, especially with the images accompanying the textual information. Needless to say, it would be hard to fit all of this on a zip drive.

So the EHR is quite an investment. Battles over who manages this information resource, and especially who pays for the development and ongoing maintenance of the record, are a large part of the political and economic debate. Meanwhile, the adoption rate is fairly low. The National Health Care Survey reported that 17 percent of physicians’ offices, 31 percent of emergency rooms, and 29 percent of hospital outpatient departments were using EHRs in 2003. These numbers have not changed much since 2003, nor did they change much in the years before 2003.

One of the chief reasons for this low adoption rate is that the return on this investment is not evenly distributed among providers, purchasers, regulatory bodies and patients. A bigger reason, I believe, is that a major portion of the return equation is being overlooked. This is where business intelligence can help.

Using Business Intelligence to Bring Out Value of EHRs

Once the EHR data is de-identified and aggregated into population statistics and patterns, entirely new possibilities emerge for generating value from that data by a number of participants in the healthcare industry. As mentioned earlier, however, analytics based on aggregated data of this kind have so far been overlooked.

Healthcare information technology generally falls into three major types of applications based on the problem they are developed to solve. For that matter, this is true of information technology in any industry. These three types are:

• Operational information technology. These are the core systems used to run the daily business functions such as paying the bills, billing insurance providers, charging patients for services, scheduling appointments, gathering patient data, handling admissions / discharges / transfers and so on. This is where the value of the EHR has so far focused from a political and economic standpoint.
  
  
• Communications information technology. Not only does an organization need IT to run the business within their four walls, but it also needs IT to communicate information to customers, suppliers, partners, regulators, specialists and so forth. This technology type multiplies the value of data in systems such as the EHR, but also increases the level of debate over who owns the data, who manages the data, and who invests in the systems for communicating the data reliably, efficiently and effectively.
  
  
• Analytical information technology. The third major technology group concerns the use of the stored operational data and communicated information to make evidence-based clinical and healthcare business decisions, including medical effectiveness, service efficiency and strategic decisions on who to serve, how to serve them, how to reach them and how to grow and evolve as an organization. Analytics represents an overlooked opportunity in the decision to adopt EHRs for healthcare organizations in all categories.

Using Aggregated EHR Data Multiplies Your Organization’s Business Intelligence

Let’s take a look at the list of potential EHR data elements again from an analytical viewpoint:

• Patient population demographics. Who are you serving? Where are they? What are the trends in the demographics? This gives your organization a better perspective on what services to perform, the volume of those services, and the people and resources needed to provide the service most effectively and profitably.
  
  
• Population health history. Once again, this gives you the ability to improve medical management and utilization based on patterns and trends in diagnoses, visits, examinations and their effectiveness in terms of health/illness progress.
  
  
• Population medication history. Are there trends in the medicines being prescribed? What is the impact of marketing drugs directly to consumers on your patient populations? What are the trends and cycles in allergies, side-effects, interactions and immunizations for your patient population?
  
  
• Aggregated laboratory test results. Are diagnostic labs being performed at a higher rate? Lower rate? What is the correlation between various types of tests and the resulting measures?
  
  
• Radiology image utilization. What patterns are there in the use of X-rays, CTs, MRIs and other images, and what impact does this have on your patient population?
  
  
• Clinical, endoscopic, laparoscopic or other clinical photographs. The same is true for this type of image as for radiology image utilization.
  
  
• Consolidated medical and healthcare recommendations for specific medical conditions and/or special care needs. Is there an increase in a certain type of recommendation with respect to particular diagnoses? Are your patients as a group getting healthier? Are there constraints (medical, financial) that prevent or promote one recommendation or another? What policy changes could change various classes of recommendations and interventions?
  
  
• Statistical analyses of appointment timings, their correlation with different types and frequencies of reminders and the impact this has on patient persistency/compliance with recommended treatments.
  
  
• Aggregated financial information. How efficient is your billing process? How efficient are the processes for contracted insurance companies? What impact does this have on your cash flow? Where are pockets of abnormally high billing and cash efficiency? How can these processes be replicated across the organization?
  
  
• Statistics on legal information gaps. Are there ways that we can help patients be better prepared with legal documentation such as advanced directives, wills, health powers of attorney, etc.? What would be the value to us as well as to patients, purchasers and payers of improving this preparedness?

Insights such as these from improved business intelligence can drive performance in terms of quality of care, efficiency of care, effectiveness of treatment, service excellence, new areas for service growth and strategic decisions regarding facilities, staffing, funding and investment in equipment.

And this is just one source of data. Combined with other types of data from the financial, clinical, research and operational systems in your organization, aggregated data from the EHR represents a significant source of value for your organization.

Next Steps

Whether you have already implemented EHR, are planning to, are thinking about it or are planning an upgrade, take another look at your organization's investment in the EHR. Look at it from the perspective of access to a wide range of analytical information to answer an even wider range of business questions. See how this could change the EHR return on investment equation for you.

Thanks for reading!

References:

Healthcare Information and Management Systems Society (2003): {{PDF|EHR Definition, Attributes and Essential Requirements, Retrieved July 28, 2006

Money alone won't solve the EHR problem

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