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EMR Information Management Tops List Of Patient Threats

Posted on March 23, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she’s served as editor in chief of several healthcare B2B sites.

A patient safety organization has reached a conclusion which should be sobering for healthcare IT shops across the US. The ECRI Institute , a respected healthcare research organization, cited three critical health IT concerns in its list of the top 10 patient safety concerns for 2017.

ECRI has been gathering data on healthcare events and concerns since 2009, when it launched a patient safety organization. Since that time, ECRI and its partner PSOs have collected more than 1.5 million event reports, which form the basis for the list. (In other words, the list isn’t based on speculation or broad value judgments.)

In a move that won’t surprise you much, ECRI cited information management in EMRs as the top patient safety concern on its list.

To address this issue, the group suggests that healthcare organizations create cross-functional teams bringing varied perspectives to the table. This means integrating HIM professionals, IT experts and clinical engineers into patient safety, quality and risk management programs. ECRI also recommends that these organizations see that users understand EMRs, report and investigate concerns and leverage EMRs for patient safety programs.

Implementation and use of clinical decision support tools came in at third on the list, in part because the potential for patient harm is high if CDS workflows are flawed, the report says.

If healthcare organizations want to avoid these problems, they need to give a multidisciplinary team oversight of the CDS, train end users in its use and give them access to support, the safety group says. ECRI also recommends that organizations monitor the appropriateness of CDS alerts, evaluating the impact on workflow and reviewing staff responses.

Test result reporting and follow-up was ranked fourth in the list of safety issues, driven by the fact that the complexity of the process can lead to distraction and problems with follow-up.

The report recommends that healthcare organizations respond by analyzing their test reporting systems and monitor their effectiveness in triggering appropriate follow-ups. It also suggests implementing policies and procedures that make it clear who is accountable for acting on test results, encouraging two-way conversations between healthcare professionals and those involved in diagnostic testing and teaching patients how to address test information.

Patient identification issues occupied the sixth position on the list, with the discussion noting that about 9 percent of misidentification problems lead to patient injury.

Healthcare leaders should prioritize this issue, engaging clinical and nonclinical staffers in identifying barriers to safe identification processes, the ECRI report concludes. It notes that if a provider has redundant patient identification processes in place, this can increase the probability that identification problems will occur. Also, it recommends that organizations standardize technologies like electronic displays and patient identification bands, and that providers consider bar-code systems and other patient identification helps.

In addition to health IT problems, ECRI identified several clinical and process issues, including unrecognized patient deterioration, problems with managing antimicrobial drugs, opioid administration and monitoring in acute care, behavioral health issues in non-behavioral-health settings, management of new oral anticoagulants and inadequate organization systems or processes to improve safety and quality.

But clearly, resolving nagging health IT issues will be central to improving patient care. Let’s make this the year that we push past all of them!

OCHIN Shows That Messy Data Should Not Hold Back Health Care

Posted on September 12, 2016 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.

The health care industry loves to complain about patient data. It’s full of errors, which can be equally the fault of patients or staff. And hanging over the whole system is lack of interoperability, which hampers research.

Well, it’s not as if the rest of the universe is a pristine source of well-formed statistics. Every field has to deal with messy data. And somehow retailers, financial managers, and even political campaign staff manage to extract useful information from the data soup. This doesn’t mean that predictions are infallible–after all, when I check a news site about the Mideast conflicts, why does the publisher think I’m interested in celebs from ten years ago whose bodies look awful now? But there is still no doubt that messy data can transform industry.

I’m all for standards and for more reliable means of collecting and vetting patient data. But for the foreseeable future, health care institutions are going to have to deal with suboptimal data. And OCHIN is one of the companies that shows how it can be done.

I recently had a chance to talk and see a demo of OCHIN’s analytical tool, Acuere, with CEO Abby Sears and the Vice President of Data Services and Integration, Clayton Gillett. Their basic offering is a no-nonsense interface that lets clinicians and administrator do predictions and hot-spotting.

Acuere is part of a trend in health care analytics that goes beyond clinical decision support and marshalls large amounts of data to help with planning (see an example screen in Figure 1). For instance, a doctor can rank her patients by the number of alerts the system generates (a patient with diabetes whose glucose is getting out of control, or a smoker who hasn’t received counseling for smoking cessation). An administrator can rank a doctor against others in the practice. This summary just gives a flavor of the many services Acuere can perform; my real thrust in this article is to talk about how OCHIN obtains and processes its data. Sears and Gillett talked about the following challenges and how they’re dealing with them.

Acuere Provider Report Card

Figure 1. Acuere Report Card in Acuere

Patient identification
Difficulties in identifying patients and matching their records has repeatedly surfaced as the biggest barrier to information exchange and use in the US health care system. A 2014 ONC report cites it as a major problem (on pages 13 and 20). An article I cited earlier also blames patient identification for many of the problems of health care analytics. But the American public and Congress have been hostile to unique identifiers for some time, so health care institutions just have to get by without them.

OCHIN handles patient matching as other institutions, such as Health Information Exchanges, do. They compare numerous fields of records–not just obvious identifiers such as name and social security number, but address, demographic information, and perhaps a dozen other things. Sears and Gillett said it’s also hard to knowing which patients to attribute to each health care provider.

Data sources
The recent Precision Medicine initiatives seeks to build “a national research cohort of one million or more U.S. participants.” But OCHIN already has a database on 7.6 million people and has signed more contracts to reach 10 million this Fall. Certainly, there will be advantages to the Precision Medicine database. First, it will contain genetic information, which OCHIN’s data suppliers don’t have. Second, all the information on each person will be integrated, whereas OCHIN has to take de-identified records from many different suppliers and try to integrate them using the techniques described in the previous section, plus check for differences and errors in order to produce clean data.

Nevertheless, OCHIN’s data is impressive, and it took a lot of effort to accumulate it. They get not only medical data but information about the patient’s behavior and environment. Along with 200 different vital signs, they can map the patient’s location to elements of the neighborhood, such as income levels and whether healthy food is sold in local stores.

They get Medicare data from qualified entities who were granted access to it by CMS, Medicaid data from the states, patient data from commercial payers, and even data on the uninsured (a population that is luckily shrinking) from providers who treat them. Each institution exports data in a different way.

How do they harmonize the data from these different sources? Sears and Gillett said it takes a lot of manual translation. Data is divided into seven areas, such as medications and lab results. OCHIN uses standards whenever possible and participates in groups that set standards. There are still labs that don’t use LOINC codes to report results, as well as pharmacies and doctors who don’t use RxNorm for medications. Even ICD-10 changes yearly, as codes come and go.

Data handling
OCHIN isn’t like a public health agency that may be happy sharing data 18 months after it’s collected (as I was told at a conference). OCHIN wants physicians and their institutions to have the latest data on patients, so they carry out millions of transactions each day to keep their database updated as soon as data comes in. Their analytics run multiple times every day, to provide the fast results that users get from queries.

They are also exploring the popular “big data” forms of analytics that are sweeping other industries: machine learning, using feedback to improve algorithms, and so on. Currently, the guidance they offer clinicians is based on traditional clinical recommendations from randomized trials. But they are seeking to expand those sources with other insights from light-weight methods of data analysis.

So data can be useful in health care. Modern analytics should be available to every clinician. After all, OCHIN has made it work. And they don’t even serve up ads for chronic indigestion or 24-hour asthma relief.

The Challenge of Patient Identification and Patient Matching

Posted on March 11, 2016 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.

A little bit before HIMSS, Healthcare Scene had the chance to sit down with a panel of experts on patient identification and patient matching, but I wasn’t able to post that interview until now. This is such an important topic, so I was happy to learn from some real experts in the space.

In this interview we talk over the challenges associated with matching patients in healthcare and the damage that’s done when you don’t match the right patient. We also talk about the solutions to the patient identification and matching problem including the impact a national patient identifier would have on the problem. Finally, we talk over CHIME’s $1 million National Patient ID challenge.

Here’s a look at those who participated in the discussion:

If you’re interested in the challenge of patient identification and patient matching in healthcare, then you’ll enjoy this discussion:

Also, after the more formal discussion we take some questions from the live audience in what we call the “after party.” Plus we even dusciss Beth Just’s new alter ego. Finally, we dive in deeper on the topic of patient identification and matching: