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Medical Groups Can Use EHR Data To Analyze Clinical Workflows

Posted on October 17, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Typically, ambulatory care organizations don’t do workflow studies, as leaders assume they have neither the time nor the data available to make it happen.

They may have more options than they think, however. A group of researchers has concluded that timestamp data found in their EHR can be used to predict ambulatory workflow.

The research article, which appears in the Journal of the American Medical Informatics Association, notes that workflow studies typically require large amounts of timing data which are too expensive to collect through observation or tracking devices. Historically, ambulatory care organizations have had to make do with observation and intuition rather than sophisticated interventions.

In fact, the relationship between health IT and ambulatory care workflow redesign hasn’t been a friendly one. A 2015 study by the Agency for Healthcare Research and Quality concluded that health IT implementations could make a mess of existing workflows. Problems included “a redistribution of clinicians’ and clinic staff’s time on different clinical tasks, repurposed usage of workspace, increased level of interruptions, multitasking, and off-hours work activities.”

According to the current group of researchers, however, these organizations may have the data they need at their fingertips. The study, which used EHR timestamp data to predict ambulatory workflow timings, suggests that this approach is valid.

To conduct the study, the researchers studied the workflow at four outpatient ophthalmology clinics associated with the Oregon Health and Science University, observing their workflows and timing each workflow step. They then mapped the EHR timestamps to workflow steps to see how they compared.

They found that workflow times generated by EHR timestamp analysis were within three minutes of observed times for greater than 80% of the appointments. What variance they did observe between observed times and timestamps seems to have been due to EHR use patterns.

Even giving these variances, ambulatory care organizations can get a lot of value out of EHR timestamp data, researchers said. “EHR timestamps…can be used to create simulation models, analyze HR use, and quantify the impact of trainees on workflow,” they concluded.

Even given this option, few ambulatory care organizations are likely to conduct formal workflow studies unless they’re backed by a deep-pocketed health system. Most medical practices have their hands full collecting what they’re owed by health plans and managing operations on a day-to-day basis.

This isn’t to suggest that they are unsophisticated, but rather, that workflow studies may require a level of time, commitment and resources that smaller practices simply don’t have. Most U.S. medical practices are small businesses.

Still, it’s good to know that if they choose, medical groups can use data already available in their EHR to make meaningful workflow improvements. Perhaps it’s time for vendors to step forward and support the use of EHRs for this purpose.

Is Claims Data Really So Bad For Health Care Analytics?

Posted on June 12, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site ( and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Two commonplaces heard in the health IT field are that the data in EHRs is aimed at billing, and that billing data is unreliable input to clinical decision support or other clinically related analytics. These statements form two premises to a syllogism for which you can fill in the conclusion. But at two conferences last week–the Health Datapalooza and the Health Privacy Summit–speakers indicated that smart analysis can derive a lot of value from claims data.

The Healthcare Cost and Utilization Project (HCUP), run by the government’s Agency for Healthcare Research and Quality (AHRQ), is based on hospital release data. Major elements include the payer, diagnoses, procedures, charges, length of stay, etc. along with potentially richer information such as patients’ ages, genders, and income levels. A separate Clinical Content Enhancement Toolkit does allow states to add clinical data, while American Hospital Association Linkage Files let hospitals upload data about their facilities.

But basically. HCUP data revolves around the claims from all-payer databases. It is collected currently from 47 states, and varies on a state-by-state basis depending on what data they allow to be released. HCUP goes back to 2006 and powers a lot of research, notably to improve outreach to underserved racial and ethnic groups.

During an interview at the Health Privacy Summit, Lucia Savage, Chief Privacy Officer at ONC, mentioned that one can use claims data to determine what treatments doctors offer for various conditions (such as mammograms, which tend to be underused, and antibiotics, which tend to be overused). Thus, analysts can target providers who fail to adhere to standards of care and theoretically improve outcomes.

M1, a large data analytics company serving a number of industries, bases a number of products in the health care space on claims data. For instance, medical device companies contract with M1 to find out which devices doctors are ordering. Insurance companies use it to sniff out fraud.

M1’s business model, incidentally, is a bit different from that pursued by most analytics organizations in the health care arena. Most firms contract with some institution–an insurer, for instance–to analyze its data and provide it with unique findings. But M1 goes around buying up data from multiple institutions and combining it for deeper insights. It then sells results back to these institutions, often paying out taking in payment from the same company.

In short, smart organizations are shelling out money for data about billing and claims. It looks like, if you have a lot of this data, you can reliably lower costs, improve marketing, and–most important of all–improve care. But we mustn’t lose sight of the serious limitations and weaknesses of this data.

  • A scandalously amount of it is clinical just wrong. Doctors “upcode” to extract the largest possible reimbursement for what they treat. A number of them go further and assign codes that have no justification whatsoever. And that doesn’t even count outright fraud, which reaches into the billions of dollars each year and therefore must leave a lot of bad data in the system.

  • Data is atomized, each claim standing on its own. A researcher will find it difficult to impossible (if patient identifiers are totally stripped out) to trace a sequence of visits that tell you about the progress of treatment.

  • Data is relatively impoverished. Clinical records flesh out the diagnosis with related conditions, demographic information, and other things that make the difference between correct and incorrect treatments.

But on the other hand, to go beyond billing data and reach the data utopia that reformers dream about, we’d have to slurp up a lot of complex and sensitive patient data. This has pitfalls of its own. Little clinical data is structured, and the doctors who do take the effort to enter it into structured fields do so inconsistently. Privacy concerns also raise their threatening heads when you get deep into patient conditions and demographics. So perhaps we should see how far we can get with claims data.

Modern Information Technology Endorsed by Government Health Quality Agency

Posted on April 22, 2014 I Written By

The following is a guest blog post by Andy Oram, writer and editor at O’Reilly Media.

If you want to see a blueprint for real health reform, take the time to read through the white paper, “A Robust Health Data Infrastructure,” written by an independent set of experts in various areas of health and information technology. They hone in, more intently than any other official document I’ve seen, on the weaknesses of our health IT systems and the modernizations required to fix them.

The paper fits very well into the contours of my own recent report, The Information Technology Fix for Health. I wish that my report could have cited the white paper, but even though it is dated November 2013, it was announced only last week. Whether this is just another instance of the contrasting pace between technologists and a government operating in a typically non-agile manner, or whether the paper’s sponsor (the Agency for Healthcare Research and Quality) spent five months trying to figure out what to do with this challenging document, I have no way of knowing.

The Robert Wood Johnson Foundation played an important role organizing the white paper, and MITRE, which does a lot in the health care space, played some undescribed role. The paper’s scope can almost be described as sprawing, with forays into side topics such as billing fraud, but its key points concern electronic health records (EHRs), patient ownership of information, and health data exchange.

Why do I like this white paper so much? Two reasons. First, it highlights current problems in health information technology. The authors:

  • Decry “the current lack of interoperability among the data resources for EHRs” as leading to a “crippled” health data infrastructure (p. 2), and demand that “EHR software vendors should be required to develop and publish APIs for medical records data, search and indexing, semantic harmonization and vocabulary translation, and user interface applications” (p. 44).

  • Report with caution that “The evidence for modest, but consistent, improvements in health care quality and safety is growing.” Although calling these “encouraging findings,” the authors can credit only “the potential for improved efficiency” (p. 2 of the paper).

  • Warn that the leading government program to push health care providers into a well-integrated health care system, Meaningful Use, fails to meet its goals “in any practical sense.” Data is still not available to most patients, to biomedical researchers, or even to the institutions that currently exchange it except as inert paper-based documents (p. 6). The authors recommend fixes to add into the next stage of Meaningful Use.

  • Lament the underpopulated landscape of business opportunities for better interventions in patient care. “Current approaches for structuring EHRs and achieving interoperability have largely failed to open up new opportunities for entrepreneurship and innovation” (p. 6).

Second, the paper lays out eminently feasible alternatives. The infrastructure they recommend is completely recognizable to people who have seen how data exchange works in other fields: open standards, APIs, modern security, etc. There is nothing surprising about the recommendations, except that they are made in the context of our current disfunction in handling health information.

A central principle in the white paper is that “the ultimate owner of a given health care record is the patient him/herself” (p. 4), a leading demand of health reformers and a major conclusion in my own report. Patient control solves at one stroke the current abuse of patient data for marketing, and allows patients to become partners in research instead of just subjects.

The principle of patient control leads to data segmentation, a difficult but laudable attempt to protect the patient from bias or exploitation. Patients may want to “restrict access to certain types of information to designated individuals or groups only (e.g., mental health records, family history, history of drug abuse) while making other types of information more generally available to medical personnel (e.g., known allergies, vaccination records, surgical history)” (p. 33).

This in turn leads to the most novel suggestion in the paper, the notion of a “patient privacy bundle.” Because most people have trouble deciding how to protect sensitive parts of their records, and don’t want to cull through all their records each time someone asks for research data, the health care field can define privacy policies that
meet common needs and let patients make simple choices. Unfortunately, a lot of hurdles may make it unfeasible to segment data, as I have pointed out.

Other aspects of the white paper are also questionable, such as their blithe suggestion that patients offer deidentified data to researchers, although this does appeal to some patients as shown by the Personal Genome Project. (By the way, the authors of the white paper mischaracterized that project as anonymous.) Deidentification expert Khaled El Emam (author of O’Reilly’s Anonymizing Health Data) pointed out to me that clnical and administrative data involves completely different privacy risks from genomic data, but that the white paper fails to distinguish them.

I was a bit disappointed that the paper makes only brief mentions of patient-generated data, which I see as a crucial wedge to force open a provider-dominated information system.

The paper is very research-friendly, though, recognizing that EHRs “are already being supplemented by genomic data, expression data, data from embedded and wireless sensors, and population data gleaned from open sources, all of which will become more pervasive in the years ahead” (p. 5). Several other practical features of health information also appear. The paper recognizes the strains of storing large amounts of genomics and related “omics” data, pointing out that modern computing infrastructures can scale and use cloud computing in a supple way. The authors also realize the importance of provenance, which marks the origin of data (p. 28).

Technologists are already putting in place the tools for a modern health IT system. The white paper did not mention SMART, but it’s an ideal API–open source, government-sponsored, and mature–through which to implement the white paper’s recommendations. The HL7 committee is working on a robust API-friendly standard, FHIR, and there are efforts to tie SMART and FHIR together. The Data Distribution Service has been suggested as a standard to tie medical devices to other data stores.

So the computer field is rising to its mission to support better treatment. The AHRQ white paper can reinforce the convictions of patient advocates and other reformers that better computer systems are feasible and can foster better patient interventions and research.

EHR Interoperability Benefits Not Related to Physician Data Sharing

Posted on February 5, 2013 I Written By

John Lynn is the Founder of the blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of and John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I always love when someone can take a subject and expand my thinking on that subject. Whenever I thought about EHR interoperability I always thought about it from the perspective of a physician sharing that data with another physician. In this case it would be one EHR sharing with another EHR (possibly with an HIE in the middle). In a recent post, Dr. Doug Fridsma from ONC, (I love that ONC blogs) expanded my thinking when it comes to the possible benefits associated with data standards and EHR data sharing when he shared the following list:

  • Patient safety advocates may want to use EHR systems to collect patient safety information, leveraging existing standards like the AHRQ “common format” for patient safety reporting
  • Providers and researchers may want to use the EHR systems to collect data for clinical research, including patient-centered outcomes research, and to identify patients who could benefit from participating in a research study
  • Providers may want to give referrals to their patients for community services, like smoking cessation or weight management programs, after discussing these topics with them during an office visit
  • Providers working with disease surveillance case report forms may wish to collect additional information about reportable conditions, such as infectious diseases
  • Provider’s office staff can use EHR’s to gain pre-authorization of certain kinds of medical devices where health payers may want to leverage clinical information collected in EHRs to support additional review of expensive medical equipment.

After just publishing my recent post about The Coming Physician EHR Revolt, I can’t help but ask what any of the above items do to help a doctor. The last one could help a physician’s workflow, but the rest of them have limited specific value to a physician. This is one of the challenges with EHR data sharing. Doctors don’t buy and implement an EHR because they want to give better referrals to their patients for community services. There’s a mismatch between providers’ needs and healthcare data exchange desires.

ONC Plan Focuses On Health IT Safety

Posted on January 2, 2013 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

The ONC has decided that it’s time to move health IT safety up to the next level, proposing a plan that would standardize the way health IT safety incidents are reported and make it easier to report straight from an EMR. And brace yourselves, vendors: this could include changing the EMR certification process to include the ability to make such reports easily.

The agency’s Health IT Patient Safety Action and Surveillance Plan is designed to strengthen patient safety efforts, including patients, providers, technology companies and healthcare safety oversight bodies in the mix. The idea, not surprisingly, is to use health IT to make care safer.

The ONC’s key objectives include the following:

*  Making it easier for clinicians to report patient safety events and risks using EMRs

Right now, it’s not exactly easy for clinicians to create a safety event report when something goes wrong in their use of an EMR, and the data they do sometimes produce isn’t easy to work with or compile.  ONC is proposing using certification criteria to make sure that whenever possible, EMRs make it easy to report safety events using the Agency for Healthcare Research and Quality’s standardized Common Formats.

*  Getting health IT developers to support patient safety and safety reporting

Within 12 months, the ONC plans to create a code of conduct — working with professional groups and health IT developers — which will hold developers accountable for:

— Creating usable, safe designs for products and adverse event reporting
— Working with a Patient Safety Organization to report, aggregate and analyze health IT related safety events
—  Scrapping practices that discourage provider reporting of safety events, such as limits in nondisclosure clauses and intellectual property protections
—  Participating in efforts to compare user experiences across different EMR systems

There’s plenty more to consider in this report, but I’ll leave you with these details in the hope that you’ll read it yourself.  As you’ll see in the introduction, you have until February 4th to comment on ONC’s plans. I hope plenty of readers do — this is important stuff.

e-Prescribing: Some Considerations

Posted on February 13, 2012 I Written By

Priya Ramachandran is a Maryland based freelance writer. In a former life, she wrote software code and managed Sarbanes Oxley related audits for IT departments. She now enjoys writing about healthcare, science and technology.

I’m always in the mood for stories, which is why I love the Cases and Commentaries section on the AHRQ WebM&M site. There’re a bunch of February posts up there but the one that caught my eye was one titled E-prescribing: E for Error?

The case involved a 63 year old man who went in to see his primary care physician. He was receiving psychotherapy, but was still prone to anxiety. The PCP prescribed him alprazolam for the anxiety. Since the clinic had just implemented a new e-prescribing system, the doctor assured the patient that he didn’t need a paper prescription and just needed to show up at pharmacy and pick up his order.

So far so good.

Back at the doctor’s office, a nurse entered the presribed medication into the practice’s shiny new system, except that she inadvertently added an order of atenolol, intended for a different patient, to this patient’s order. She soon realized her mistake and deleted the atenolol order.

When the patient went to the pharmacy, he was given both the alprazolam and the atenolol, which he thought was odd, since he had been prescribed only one medication. However, he just went ahead with taking both medications per the directions handed to him by the pharmacist, and it was only a few days later, during an appointment with a cardiologist that the mistaken atenolol addition was finally identified.

Fortunately, the patient lived to tell the tale, which we all know is not the outcome in some sad cases. Elisa W. Ashton, the author of this Cases and Commentaries piece, has some great points listed as her takeaways from this case. Here are mine:

It’s too soon to say goodbye to paper. I worry about trees more than the average Jane, but if there’s a ever a case to be made for a paper prescription, here it is. A paper prescription would’ve shown up the double prescription both to the nurse, as well as the patient, making it less likely to make it to the pharmacy.

It’s not clear who/what failed. Did the nurse realize delete the wrong entry only after she transmitted the patient’s prescription? Did the prescription software trule delete the medication or simply mark it as flagged for deletion?

– This accident happened on a newish system, perhaps users were not as familiar with it as they should have been.

If you think something’s odd about your prescription, speak up. As patients many of us tend to assume that doctors know best. However, doctors are as human as everyone else, no matter how many initials tag along before or after their names. You don’t have to be obnoxious about it, it’s perfectly fine to verify politely with your doctor’s office if the additional (or missing) medications are necessary.

– Bravo to the eagle-eyed cardiologist! It was great someone caught this error in time, though I would much prefer that some kind of check system be built into the e-prescription system to prevent errors of this sort.

Go check out the post on AHRQ.