HealthyData Blog
In healthcare, there are so many regulations and technologies to stay current on. In our HealthyData blog, you will read everything from CMS meaningful use to EMR news, clinical best practices and QAPI program initiatives.
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.
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 [accurate-data] the delivery system and to develop plans for the provision of more efficient and effective patient care.”
Let’s imagine that you like the features of your transplant database and your hospital is transitioning your department away from your comfortable and dependable software, into an enterprise application. You’ve likely heard this transition makes sense and will provide costs savings for the entire organization, but you might be wondering if the cost savings will benefit your department. There are obvious economies of scale with an enterprise solution, but you can’t help but wonder if this will create new problems for you. Having access to clinical data within the continuum of care is wonderful, but it hasn’t eliminated the need for a fax machine.
One of the universal issues we face when asked to help a program that has below standard outcomes, is how that program handles workflow and data management challenges. A big part of managing a transplant program is collecting and managing all the information needed to make good decisions. For instance, regardless of the type of transplant, one common factor that determines who receives the Gift of Life, and when, is the Waitlist. Management of this seemingly simple list can become a daunting task[waitinglist] when one considers the complexities of not only transplantation itself, but also of the information management required to determine appropriate candidates to place on the waitlist and ultimately carry out that transplant.
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.
You've just installed a new electronic health record and a dedicated module for transplant.
Now you discover your long term medical secretary, who is capable of typing 90 wpm, is planning to retire. What do you do? High turnover in any service line is a problem, but when you have a constant flow of paper in an environment that requires up-to-the-moment patient data, it is only a matter of time until patient safety becomes your primary sense of urgency. What are your options?
A pressing issue in transplantation today is appropriate organ allocation and ensuring that the sickest patients and those who would benefit the most are afforded the gift of life. In order to be listed, all patients require certain specific data points in order to be successfully entered into the national database and deemed active on the waitlist. For some patients, such as those awaiting a lung or liver transplant, organ allocation is driven by scoring systems (the LAS and MELD score respectively) that are a reflection of disease severity.
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.
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.
Beware as I sound off about a sensitive topic, but doesn’t it feel like access to outpatient data and lab results is more difficult than ever? The problem remains: a significant portion of relevant patient information is not being presented to the care team promptly in a format that will provide the most benefit. There is a world of difference for time savings and quality of care between having discrete information in the Electronic Health Record compared to having to search for pages of data or documents. The difference is having data immediately available for overviews, reports and trending. Having patience and a good attitude will go a long way while waiting for structured and usable data.
Enterprise applications are driving change in most every hospital, and transplant programs are no exception. I’ve spoken to many nurse coordinators, and I often hear they are coping with change; many are adopting new systems in progress, or will be soon. I get to speak to a variety of healthcare personnel as part of my job and it seems that the only constant these days, is change. Changes in software, changes in workflow, changes in management, and merging of institutions are common. Sometimes change is for the better of the organization but is it always welcome for the service line or more importantly, the transplant team working with patients.
The role of the cancer registrar is essential in the effort to gather vital information for treatment planning, research studies and in the conduct of clinical trials. Further, as quality of data is more closely looked at in this era of monitoring patient outcomes with a financial eye, already overburdened cancer programs and state registries are doing their best to cope with an ever increasing volume of work. The new CTR standard seeks to ensure the quality of this data collected by cancer registrars is optimized.
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.
