With electronic EMRs, healthcare organizations are experiencing high demands for analytics with insufficient staffing to meet needs in a timely manner. Department backlogs for larger organizations reach the hundreds, and while smaller organizations have smaller queue backlogs, they have fewer resources resulting in similar lengthy wait times up to several months. An overwhelming backlog can lead to complaints from leadership and hinder timely decision-making. Fortunately, there are several effective strategies that organizations can implement to manage queue backlogs and ensure timely access to essential data.
1. Federate Your Analytics Delivery Model
Organize your analytic resources into small, agile product-driven delivery teams, which are part of the analytics ecosystem yet sit within the business/clinical areas that you support. By bringing decision-making closer to the work, these teams can scale to meet demand and spin-up or spin-down as needed. You can eliminate the need to satisfy every request in ever-increasing queue backlogs by doing the following for each delivery team:
- Establish a product owner and development lead to prioritize/own and oversee delivery respectively
- Create an advisory team that can help guide as needed
- Leverage an executive sponsor to identify the top priorities for the organization, promote adoption and help overcome roadblocks
2. Be Selective About Requests
To attract and retain talented analytic resources for these teams, foster innovation and focus on intrinsic motivation. Instead of addressing every request in a first-in/first-out manner, build products that bring measurable value and drive action. Encourage customers to self-serve by educating and empowering them to answer their own questions rather than submitting requests for every inquiry.
Analytic resources often get a complex and intricate request, yet its purpose is often unclear.While it’s nice to put your head down and crank away at a to-do list, we need to stop and think critically about the bigger picture for each request. If an analytics deliverable isn’t going to accomplish one of the below items, is it necessary?
- Used in a workflow
- Impacts clinical decision making / improves patient outcomes
- Program metrics to monitor efficacy
- Used to make a business decision around resource allocation
If there is no arguable business need, it’s okay to say “no.” “I’m just curious” requests result in hundreds (if not thousands) of hours wasted each year in analytics departments.
3. Educate and Empower Customers via Self-service
Encourage customers to use existing reports and save their own private copy with modifications such as altered criteria, columns and summaries. Analysts should share their screen with the customer and explain each click as they build the first report together, e.g., in Cogito, off Tableau extracts, etc., so end users understand tool features and functionality. Education should include how to decide which tool is appropriate, how to modify existing reports to suit individual needs, how to save and access the report later, and how to share the content with colleagues. Time invested up front in educating a customer pays off ten-fold. This is because they’ll have a better understanding of reporting tools and limitations, as well as have the capability to create their own reports in the future, preventing additional requests.
4. Streamline Processes for Queue Backlogs
Red tape and protocols can have both positive and negative effects on outcomes, regardless of the profession. While policies exist to ensure a quality product and protect us, it’s important to regularly assess their appropriateness and effectiveness. For example, if adding a column to a report takes two minutes but requires two days to get to production, the process isn’t efficient. Garner feedback from your organization’s analytic resources to identify pain points in the build process. You might find that the change committee approval implemented years ago is no longer effective or necessary. Or perhaps there is a missing piece of security causing cumbersome workarounds.
5. Group Troubleshooting Sessions
Egos are a funny thing. They tell us we can figure it out on our own and don’t need to ask for help. While you might be able to figure it out eventually, but the subject matter expert can do so faster. Encourage your team to use group troubleshooting sessions when they’re struggling. No one can be an expert in all clinical workflows, ordering, pharmacy, etc. Lead by example and humble yourself to ask for help when appropriate. Group troubleshooting is not limited to reporting specialists. It’s helpful to include the requestor, app analysts, clinical end users and anyone else with valuable knowledge of the data and request. By leveraging the collective expertise of the team, you can identify solutions faster and improve the overall quality of your work.
6. Pursue Progress, Not Perfection with Queue Backlogs
Analytic resources are critical thinkers and strive for perfection in their deliverables. Sometimes, perfection in a deliverable can be achieved. However, with limitations in tool functionality, variation in clinical documentation and other unavoidable variables, perfection isn’t always possible. Reports aren’t a band-aid for broken workflows. While we like to think we’re magical, we can only do so much. If a solution fulfills 95% of someone’s request, that may have to be enough. Prior to investing another 20 hours into perfecting a product build, weigh the benefit of gaining the extra 5% to achieve perfection versus accomplishing several other deliverables within that same time frame and providing greater value to other customers.
Final Thoughts on Reducing Queue Backlogs
These strategies are not a quick fix. They require a shift in mindset which must be adopted by the entire enterprise to be most effective. If one analytic resource does whatever the customer asks without requiring a business case and without educating the customer, that customer will develop the expectation that all data will be spoon fed to them in the future. Messaging must be consistent across the organization so customers know what to expect and will adapt accordingly over time. Implement these strategies as soon as possible. In doing so, you can stop the hemorrhaging, and you’ll have fewer counts in your report queue.
Learn more about how our team can help your organization optimize its resources and deliverables to accelerate your data innovation journey.