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.
As we’ve seen in many other industries, when one needs to do more with less, often the best 
step is to employ computer-aided tools to assist in the process. Excel spreadsheets make much lighter work of simple accounting, computer-aided design tools speed the process of architectural and mechanical engineering development and ERP systems do the yeoman’s work of tracking inventory and manufacturing production.
Computer-aided abstraction tools can likewise review patient records to derive critical information for review and to populate registries. To be sure, computer-assisted coding (CAC) has already been deployed widely to find some of the same data, but for the purpose of accurate billing for services rendered. And case finding and cancer management tools like MetriQ or Abstract Plus may be used for abstraction of documents that are fully electronic already.
But what about the significant load of patient records presented to the MedOnc or RadOnc departments that have been faxed or hand-carried by a patient? They can be scanned by the HIM department, but perhaps not in the time frame required by the CTR to effectively meet performance and quality compliance guidelines. And the resulting scan is most commonly a static image that is attached to the EHR or specialty management software. The discrete data required is not available to the CTR and therefore must be found and entered manually.
Computer-Aided Abstraction tools that can automatically capture the discrete clinical data from either electronic or paper documents for the Certified Tumor Registrar to review provides significant efficiencies. These tools present the required patient information faster, more accurately and completely than manually reviewing the record and hand-entering data.
Using either an Advanced Rules Engine or Natural Language Processing (NLP) tool to identify the appropriate phrases and context can go a long way to lightening the load of today’s CTR staff. This will more efficiently provide ALL the data required in the population of cancer registries and can also enhance the efforts for research and clinical trials.
Shortly, Extract Systems will be exhibiting this multidisciplinary approach to capturing synoptic data from electronic and paper records. Let us know if you’d like to attend this webinar.
