Post-SIIM 2013 Annual Meeting Reflections

Another great SIIM annual meeting is behind us and it was great, as always. I am going to post some thoughts and reflections this week.

Today, I have been thinking about analytics and, in particular, the use of a workflow engine and a standardized set of terms and definitions (such as what is being defined in SWIM) to ensure analysis of workflow events (type, timing, relationships, patterns, etc.) consistently across systems.

There were several great talks by Dr. Brad Erickson and Chris Meenan and others on the topic and these were followed by a large turnout of engaged attendees for a SWIM demo (see pic below).

...SWIM lessons
…SWIM lessons

My thoughts…

  • The use of a mature, off-the-shelf (open source or commercial) workflow engine has been considered by PACS and RIS vendors, with some attempting to use them in their product. It has not been widely adopted for two main reasons (I believe)…
  1. Most PACS from large vendors were bought, not built by them—the risk of replacing the built in logic with an external engine without introducing functional regression is high (read as: it would be expensive);
  2. Unless the workflow engine spans several systems, it would not have the full benefit (see more on this below).
  • The workflow examples cited often started with the arrival of the image objects from the modality (initial event that starts the workflow channel). Ideally, the workflow engine extends to before the order is placed, managing the order placement, decision support to ensure the right procedure is ordered, scheduling, protocoloing, and acquisition, along with the reading and post-processing steps. It should also span to the results distribution and archiving, managing the timing and destinations of the report and the lifecycle of the historic imaging data.
  • One of the limitations of using a parallel image management pipeline (e.g. sending images through a system before arriving in PACS) in order to detect the event that triggers the workflow can introduce some points of failure. Consider if the system integrated with the workflow engine goes down and images don’t get to the PACS—this outage would limit the value of the integrated image management and workflow engine system. A possible solution is to extend PACS and other systems, such as the RIS, EMR, CDS, VNA, Enterprise Viewer, document management system, etc. to expose the event information. This would allow the workflow engine to apply the desired workflow rules and orchestrate the data flow and work steps without being a potential bottleneck.

More thoughts from SIIM later. Stay tuned.

Article – CHIME seeks Stage 2 delay, defends MU

So, the U.S. government—CMS/ONC and some Senators—and CHIME (College of Healthcare Information Management Executives) are “discussing” the merits and best timing of HITECH and Meaningful Use.

This article provides a good summary of the questions and recommendations posed.

Some key points from the article and my thoughts…

  • The Senators are fairly looking for evidence of results from the significant investment of taxpayer dollars. The reality is that this change is large and multifaceted. It will take time to reap the benefits once operations are normalized and productivity is enhanced.
  • CHIME believe that there are merits to the government’s programs, but wants to slow the pace of change. I know from personal conversations with smart, effective folks working for respected providers that they are reeling from the number of implementation projects driven by ACO, MU and other initiatives that they have going right now. The troops may indeed need a short break and to reflect on lessons learned from the initial change.
  • “CHIME also urged Congress to request an update from ONC regarding what technologies, architectures and strategies exist to mitigate patient matching errors” …it is interesting that CHIME is looking for this, as MPI (Master Patient Index)—also known as PIX (Patient Identifier Cross-Referencing) in the IHE Technical Framework—has been around for years and used in many projects to enable sharing of patient records across patient ID domains

Article – Radiology Staffing: How to Do More with Less

A lot of people are talking about using analytics to make operational improvements (read as: lowering costs while improving quality of service), but this article describes some specific ways to do this within a Radiology practice.

Examples (from the article)…

  • Use actual procedure data to determine the specialty needed, as well as the number of staff needed in each facility/location. It also helps determine if full-time or part-time staff are needed.
  • Adapt the daily shift schedule based on hourly exam volume peaks.

The article also explains how technology is used to improve efficiency…

  • Cloud based image sharing, integrated with PACS, to distribute reading of exams among distributed Radiologists.
  • Shared worklist across facilities

Article – CPOE use can reduce unneeded CT scans

Not a mind-blowing revelation, but when doctors are told that the information they want already exists, they don’t order more tests (usually).

And while the results of the study summarized in this article reflect only a small decrease in new CT exams being ordered (“physicians canceled orders after receiving the alerts about 6 percent of the time, making for a net cancellation of 1.7 percent of studies. In a control group, physicians canceled only .9 percent of alerts.”), every bit counts.

And it reduces the radiation the patient receives, as well as helps keep the Radiology schedule free for really important exams.

A goal to simply reduce the number of exams performed is misguided. This blog post summarizes a proposed model to help separate the necessary from unnecessary exams.

Article – The 8 RIS innovations you need now

Here is a summary (note: may need to register with site to access) of some RIS (Radiology Information System) innovations that providers should be looking for.

Sneak peek…

  1. Digital dashboards
  2. Electronic medical record aggregation
  3. Clinical decision support
  4. Critical results reporting
  5. Customer service
  6. Technologist feedback
  7. Peer review
  8. Data mining, surveillance, and outcomes

I am working on an article on how (and why) RIS and PACS will be deconstructed and will not exist (as we know them today) in the future. Stay tuned for that.

Article – Creating a Clearer Picture of Patient Flow

This is cool.

It would be interesting to see the convergence of the output of SIIM‘s SWIM initiative and this application to understand real-time metrics of a Radiology department. The dashboard could show the actual location of patients, their spot in the prescribed workflow, and the comparison to statistical norms and/or KPIs.

Layered on top of a BI (business intelligence) platform for historic data analysis, and you would have something special.