HealthyData Blog | Extract Systems

The ultimate juggling act: clinical labs in a hospital setting are required to maintain the highest operational standards. They complete their own inpatient testing while managing the logistics of send-outs and the returning results from reference and specialty labs. No matter where it’s coming from, comprehensive data needs to get back to the ordering physician -  data required to make the best care decisions.

How do clinical labs provide quick and consistent information to providers if some of the data is interfaced and some of it is not? Interfaced clinical lab data travels smoothly throughout the organization to the ordering physician and other providers with permission to access it for the purposes of treating that patient. The data is secure and discrete, allowing providers to use it, graph it, analyze it and compare it to other data relative to that patient. But what if the data is stuck in a faxed document from an external lab? Information arriving this way loses those qualities and creates extra work to open it, find the valuable pieces of information, and consider its implications for that patient.

Tools are available to help clinical labs with the job of running their labs. Lab Information Systems and Analytical Tools enable them to enhance operational, clinical and financial outcomes. But what about the special issues caused by needing to manage your own data AND dealing with incoming results from send-outs?  An LIS or EHR gives you a repository for incoming information and facilitates the sharing of that information with providers, but what tools exist to help clinical labs get the information arriving from many sources into those systems?

A way to better serve the clinical staff is to convert external lab results into the same format (discrete, electronic) and input that information into the same systems as inpatient lab results. A solution for this problem lies in the workflow optimization and automated data capture provided by an Advanced Clinical Data Capture solution. Advanced data capture allows for faster, easier conversion and validation of incoming data into your LIS or EHR for immediate access by your clinical staff, the same way they would access it if the data came from inpatient sources.

I was researching topics around data accuracy in healthcare the other day and came across this AHIMA article from 1997 (did we even have the internet back then? J). The article was focused on a growing demand for accurate coded data and as I read the article, I felt that AHIMA was ahead of its time and like I was in a time warp. They were stating the importance of data accuracy and consistent coding to speak about how “these data are used to assess resource utilization and outcomes throughout the delivery system and to develop plans for the provision of more efficient and effective patient care.”  They go on to speak about the “analysis of outcomes has become a major focus because of the relationship between better outcomes, improved efficiency, and lower healthcare costs. Improving the quality of care and managing diseases in such a way that expensive complications can be avoided reduces healthcare costs.” Sound familiar to anyone?

Interoperability, yes, but with truly accurate data
A mid-Atlantic hospital’s transplant program recently went live with Epic Phoenix to better track pre and post-transplant patients. Prior to the Epic implementation, all patient data was maintained manually on paper flowsheets.

As I wrote recently, the Certified Tumor Registrar shortage is currently being exacerbated by the Commission on Cancer’s CTR Standard 5.1. I’m not saying the standard is not a worthy step to ensure that the abstraction of patient records to local registries and the NCDB is accurate - just that it clips the available pool of in-house or outsourced personnel to keep up with the load.

Every patient has a history. Getting those histories moved from other systems and sources is an important consideration when implementing a new EHR. Whether your healthcare organization is deploying a new EHR, replacing one or implementing a new module in an existing EHR, your ability to capture data into the fields of the new EHR/module will define how fast you can use that history to make better care decisions.

Besides, can you imagine your new EHR/module being empty? I think not. Therefore, those deploying new systems invest in data conversion tactics that move as much information into the right spots to make the new system useful. Planning recommendations and checklists for that process are well documented on healthit.gov and many EHR and 3^rd party conversion vendors’ websites.

Your plan: you define the data that will be converted and migrated, including patient demographics, lab results, radiology results, medications, immunization records, allergy lists, ECG and vitals, and other clinical and business documentation.

If data is already structured and can be easily mapped to the new system, then the migration is typically simple. But what if it is not? What if data from the old system needs to be parsed differently for the new system? What if some important data is still stuck on paper or in other unstructured formats?

While many organizations have begun to deploy advanced clinical data capture for day-forward needs, it's equally if not more valuable in converting unstructured or re-mapping structured data to a new system. The advantages of an advanced clinical data capture system include its ability to read unstructured clinical data and find the vital information that is needed for discrete fields of an EHR or specialty database based on the defined criteria set by the organization that has deployed it. Applying the same principles for data conversions speeds up the conversion process, plus the built-in validation and Quality Assurance functionality helps ensure the data is 100% correct. The ultimate goal: A new system with the right historical information so that the right quality of care decisions can be made.

Document management associations and consulting organizations have published a mountain of studies addressing the inefficiencies of manually keying data from unstructured documents to electronic databases. Most acknowledge that manual data entry is error prone, labor intensive and slows the workflow process. Yet, particularly in healthcare, it’s stunning how many organizations still rely on this practice day to day to populate demographic and lab results data into EHRs and other clinical decision support systems.

Effective January 1, 2015, all abstraction of medical records for cancer cases at Commission on Cancer (CoC) accredited facilities must be performed by cancer registrars who have achieved the Certified Tumor Registrar (CTR) credential.

Population health management’s success is based on many factors that fundamentally rely on data. To start, you will need data to segment higher risk patients and rising risk patients from the others. Once segmented, you may also need additional data on those specific patients to continue to assess their risk levels or consider other attributes that contribute to those risks. This data typically originates from the EHR, laboratory, e-prescribing, specialist reports, and claims data.

So, how complete is your data?
We can all agree that the more “complete” data that you have about a patient, the better chance you can use analytics to assess, manage and even prevent common population health issues.  

How can you actually get more complete data?
One existing and easily accessible source of “missing” data for any patient is clinical information that is stuck in paper or electronic documents. Although this data is “accessible” to providers as a scanned document, it does not play a role in supplying population health databases or in running analytics on the data to assess if a patient is at risk. If blood tests or consultative notes from specialists are not getting into the EHR as discrete clinical data, then your population health risk assessments cannot be accomplished easily and reliably.

Mining your existing paper and electronic documents and extracting all pertinent clinical data from them is the easiest, most cost-effective way to begin to fill the gap of missing data. You already have the data, you just need to get it into your systems!

In doing so, it is also important to create workflows that automate this process, not burden clinical staff with data entry, and ensure high accuracy and quality assurance of the data being extracted and entered. By essentially “converting” all document/paper-based information into discrete clinical data, you are on your way to better patient prioritization and better preventative care.

Got data gaps? We'd love to show you some health data management tools to get your unruly, unstructured data in line!

When considering how you get vital clinical data that arrives via “paper” (mail, fax, etc.) into the hands of providers quickly, people often say, “oh, we have scanning.” But what does that really mean?
 
Generally, “we have scanning” falls into 2 areas:

Ensuring the Safety and Effectiveness of Laboratory Data in Electronic Health Record Systems

Resource: Paper from CDC’s Lab Program Division
The CDC’s Division of Laboratory Programs recently published a paper: The Essential Role of Laboratory Professionals: Ensuring the Safety and Effectiveness of Laboratory Data in Electronic Health Record Systems. This paper offers excellent cautions, guidelines and advice for both lab professionals and IT staff who work on LIS or EHRs.

Emphasis on three topics

• Engagement: lab professionals have expertise; don’t be afraid to share yours!
• Data Integrity and Usability: lab professionals can guide and maintain data integrity. It’s your data; don’t let it be compromised.
• Innovation: lab professionals are in a unique position to partner with stakeholders to stimulate innovation. Your voice and experience are valued!

The paper is chockfull of studies and resources you can call upon when discussing these topics with your team and colleagues outside your lab. Patient stories bring abstract concepts to life. You’ll find ideas for engaging, a list of opportunities for you and colleagues to be proactive rather than reactive, and strategies you can employ for innovation.
 
It’s about you!
Your knowledge and experience give you a unique viewpoint when it comes to ensuring patient safety. Raise your hand, share your expertise, and use materials such as this paper to help guide that process.
 
You and your colleagues may also be interested in materials from ONC, such as Safety Assurance Factors for EHR Resilience (SAFER) guides. These guides help to assess patient safety risks in lab test orders and results reporting functions within provider EHRs or other health information systems.
 
In future postings of our Healthcare blog, we’ll investigate and share strategies you can employ to ensure your lab leads the way in data integrity and patient safety.