Someone in leadership just made a commitment. Maybe it was during a payer meeting. Maybe it came out of a board presentation on value-based readiness. Either way, the message landed on your desk: we need outcome data. And now you, the clinical supervisor or quality coordinator or EHR administrator who actually understands how data moves through your system, are the person who has to make it real.
The screening instruments your clinicians already administer contain the raw inputs payers are asking for. Your EHR captures most of this during routine encounters. The problem has never been collection. The problem is that the path between “clinician enters a score” and “organization reports a 12-month outcome trajectory” is full of gaps that only the people closest to the workflows can see.
What “Outcome Data” Means at the Workflow Level
When payers and regulators say “outcome data,” they mean something very specific. They mean discrete, scoreable, time-stamped clinical measures linked to treatment episodes and diagnosis codes. They mean data that can be aggregated across patients, trended over time, and reported in standardized formats.
NCQA’s HEDIS depression monitoring measure, DMS-E, illustrates the standard. It requires PHQ-9 scores captured in discrete EHR fields using specific LOINC codes during defined assessment periods across the measurement year. Scores documented in free-text progress notes, scanned paper forms, or unstructured clinical narratives do not count. The measure requires structured, queryable data that the EHR can surface without a human reading every chart (NCQA, 2025).
SAMHSA defines measurement-based care as the systematic, repeated use of validated instruments to track client progress and inform shared treatment decisions (SAMHSA, 2023). The Joint Commission, the American Psychological Association, and the VA all endorse it. The clinical evidence base is strong. The implementation evidence, however, tells a different story. Only about 18 percent of psychiatrists and 11 percent of psychologists routinely use standardized instruments for symptom assessment (Lewis et al., 2019). The gap between endorsement and practice is almost entirely a workflow problem.
Your EHR has the fields. Your workflows do not fill them consistently, link them to episodes, or surface them for reporting. That is the operational reality behind leadership’s new commitment.
Where MBC Workflows Typically Break
The failure points are predictable, and if you work in or near an EHR every day, you already know most of them.
Scores get entered in the wrong place. A clinician administers the PHQ-9, calculates the result, and documents it in a progress note narrative. The score exists in the record, but it lives in free text. No reporting tool can find it without someone reading the note. For that score to count toward a HEDIS measure or a payer report, it needs to land in a discrete, coded field.
Follow-up assessments fall off. An intake PHQ-9 gets completed reliably because it is part of the admission workflow. But re-administration at defined intervals throughout treatment depends on someone tracking when the next assessment is due and ensuring it happens. Without a systematic protocol for re-screening, outcome trajectories have gaps that make them unreportable. SAMHSA’s 2024 financing report on MBC identifies this pattern explicitly, noting that EHR integration and workflow remain persistent barriers to broader MBC adoption (SAMHSA, 2024).
Assessments get completed but not linked. A PHQ-9 score sits in a discrete field, but it is not connected to the treatment episode, the diagnosis code, or the clinician of record. When someone tries to build a report asking “what was the average symptom reduction across our depression caseload over 12 months,” the data is technically present but structurally disconnected.
The reporting layer does not exist. Even when scores are captured correctly, many organizations have no mechanism to aggregate them across patients and time periods. Individual scores accumulate in individual charts. Nobody builds the query that connects them into a population-level story.
The clinicians entering scores every day are not the problem. The workflows that treat each score as a documentation task and never connect it to a reporting outcome are the problem. Lewis et al.’s comprehensive review of MBC implementation found that organizational and workflow barriers, not clinician resistance, are the primary obstacles to sustained adoption (Lewis et al., 2019).
What You Can Fix Before Anyone Buys New Software
Most of these breaks are configuration and protocol issues, not software limitations. Your EHR almost certainly supports discrete data capture for standard screening instruments. The question is whether your organization has configured it that way and built the workflows to maintain it.
Start with a template audit. Review the clinical templates your staff use for intake and ongoing sessions. Confirm that PHQ-9 and GAD-7 scores flow into discrete, coded fields with the correct LOINC identifiers. If scores are being captured only in narrative text boxes, that is the first fix. It is a configuration change, not a software purchase.
Establish re-administration protocols. Define when screening instruments get re-administered: every session, every 30 days, at treatment plan reviews, or at another clinically appropriate interval. Some clinical guidance, including recommendations from the University of Washington’s AIMS Center, calls for re-measurement at every contact for patients in active treatment (NYS DOH, 2016). Whatever interval you choose, build it into scheduling workflows and assign responsibility for tracking completion.
Connect scores to treatment episodes. Work with your EHR administrator to ensure assessment results link to diagnosis codes, treatment plans, and the responsible clinician. This connection is what makes individual scores aggregable into the population-level reports payers are looking for.
Build or request the reporting layer. Once scores are in discrete fields and linked to episodes, the next step is a report or dashboard that aggregates them. Connecting your EHR data to a visualization platform like Xpio Analytics turns individual screening scores into trend lines, cohort comparisons, and the kind of payer-ready outcome narratives that drive contract negotiations.
Leadership made the commitment. You know where the data gets lost between the clinician’s screen and the reporting layer. That knowledge is what closes the gap between a strategic promise and a reportable outcome.
When leadership asks for outcome data next quarter, will your workflows deliver it, or will your team be reverse-engineering it from chart reviews?
Xpio Health helps behavioral health teams connect the clinical data already in their EHR to the outcome reporting workflows that payers and regulators now expect. If your screening data is not making it to a dashboard, let’s figure out where it’s getting stuck.
#BehavioralHealth #MeasurementBasedCare #ValueBasedCare #PeopleFirst #XpioHealth
References
- National Committee for Quality Assurance. Measurement-Based Care in Behavioral Health: Let’s Keep Moving Forward. NCQA. 2025. https://www.ncqa.org/blog/measurement-based-care-in-behavioral-health-lets-keep-moving-forward/
- Substance Abuse and Mental Health Services Administration. Use of Measurement-Based Care for Behavioral Health Care in Community Settings. SAMHSA. 2023. https://www.samhsa.gov/sites/default/files/ismicc-measurement-based-care-report.pdf
- Lewis CC, Boyd M, Puspitasari A, et al. Implementing Measurement-Based Care in Behavioral Health: A Review. JAMA Psychiatry. 2019;76(3):324-335. https://pmc.ncbi.nlm.nih.gov/articles/PMC6584602/
- Substance Abuse and Mental Health Services Administration. Financing Measurement-Based Care in Community Behavioral Health Settings. PEP24-01-007. SAMHSA. 2024. https://library.samhsa.gov/product/financing-measurement-based-care-community-behavioral-health-settings/pep24-01-007
- New York State Department of Health. Administering the Patient Health Questionnaires 2 and 9 (PHQ 2 and 9) in Integrated Care Settings. NYS DOH. 2016.https://www.health.ny.gov/health_care/medicaid/redesign/dsrip/2016-07-01_phq_2_and_9_clean.htm
