data + analytics: data-driven foundation, part 1
About two months ago, I woke up in a panic, thinking about how artificial intelligence (AI) will upend society and specifically, healthcare. I can’t be taken seriously talking about us becoming an AI-forward healthcare company if we’re not a data-driven organization today. So today, I’m talking about the first step in our digital transformation.
Background
I’ve long been a proponent of an organizational operating system (think: EOS, Scaling Up, Rockefeller Habits). The principles are inarguable: clearly communicate priorities, track progress, consistently report metrics, and make data-driven decisions. Our leadership team and departmental meetings all have a scorecard – a list of consolidated KPIs – nested under the strategic KPIs.
In a perfect world, it would be easy to develop and report on metrics that give a good snapshot of the organization's health. But reporting is difficult. You’ve got to determine your metrics and figure out how and when to pull them. Everyone needs to trust the numbers. Before we could embark on a digital transformation, I realized we needed to button up our reporting by creating a single source of truth.
I think data gets a bad rap in behavioral health. Often when I talk to clinicians about AI, measurement-based care, and leveraging data to promote better outcomes, their eyes gloss over. They assume I want to turn people into robots, ignoring the importance of human connection. That couldn’t be further from the truth.
The Promise of a Data-Driven Approach
Enhanced Patient Care: Data allows for personalized treatment plans and predictive insights, ensuring more effective and compassionate care.
Informed Decision-Making: Data provides a solid foundation for making informed decisions based on evidence rather than intuition.
Accountability & Transparency: Tracking KPIs ensures everyone understands why decisions are made and how they are measured.
Strategic Growth: Proper resource allocation and sustainable planning are driven by data insights.
Operational Efficiency: Identifying and addressing inefficiencies through data analysis enhances workflow and reduces administrative burden.
Progress to Date
We successfully built an ODBC connection between our electronic health records (EHR) and Power BI for reporting and analytics. Currently, our reporting focuses on clinical/medical utilization, the number of new patients scheduled, and the efficiency of our revenue cycle management. While this is just the tip of the iceberg, we’ve already seen a dramatic improvement in performance. I believe we’re getting our best patient outcomes and team morale to date, all while achieving our best financial performance.
Next Steps
We need to integrate a whoooole lot of data into our single source of truth. Below is a non-exhaustive list.
Marketing data from Google Analytics
Admissions and marketing data from our CRM
Call data from our VoIP provider, RingCentral
Bringing all the data together is just the first step. We then need to build data-driven decision-making muscle for all of our leaders. Until we can use data from THE single source of truth to answer the following questions, we’ve got work to do.
What’s the budget for patient acquisition by line of service?
How much can we invest in (speculative) technology projects?
Should we invest in digital marketing or business development?
Should we hire another INSERT POSITION (therapist, APP, physician, scheduler)?
How efficient is our revenue cycle management?
Is the INSERT POSITION doing a good job based on established KPIs?
Which providers are getting the best patient outcomes?
My Ask Internally
Take ownership of your data and deliverables. If you’re responsible for managing clinical utilization, you should know exactly how it’s calculated.
Know what you want to report on, even if you don’t know how to do it. Want to report on the effectiveness of various providers, great. Let’s discuss what we could use from our existing data and what else needs to be collected.
Understand that garbage in = garbage out, our reporting is only as good as the inputs. When we make changes to how we’re inputting data, we need to first reengineer the reports to make sure there’s no loss.
Develop a rudimentary understanding of our data architecture. Our EHR is built around patients, providers, and appointment types. If you want to change something, think upstream and downstream. For example, if we change an appointment in the system, we need to think about how that affects our scheduling and marketing metrics (upstream) and our billing or utilization metrics (downstream).
Ask questions! Reporting is difficult. The goal is not just about efficiency and hitting our numbers for their own sake. We want to hit numbers that help us bring our vision (affordable, high-quality healthcare as a right)
Down the Road
We’re just getting started with the first part of the digital transformation…
Data Integration & Reporting (BI-focused)
Cloud Architecture & Technology Stack
Digital Transformation (Modernization and Automation) of Operations
Advanced Analytics (patient-focused + BI)
