Here is a number that should make any practice administrator pause for a moment. Many U S organizations report duplicate patient record rates in the high single digits, and in some environments the percentage climbs into the teens. That is not an edge case, it is the daily backdrop for scheduling, intake, and clinical decisions.
If your clinic depends on a Master Patient Index, every duplicate or mistaken merge slows access, clutters staff workload, and quietly drags down throughput. A missed match can send a reminder to the wrong chart, hide a referral, or leave a pre visit task hanging in limbo. On the flip side, a bad merge can compress two lives into one record, something no clinician or billing team wants to untangle.
Platforms like Solum Health, which position themselves as an AI powered unified inbox and intake automation layer for outpatient facilities, live or die on the quality of that identity data. If the MPI is not trustworthy, automation cannot route messages, trigger intake, or push data into the EHR and PM systems with confidence.
A Merge Unmerge Policy is the practical answer. It is the organization’s written rule set for when two records can become one, when they must be separated again, and how every step is documented so you can defend it later.
At its core, a Merge Unmerge Policy for an MPI does four things.
I will walk through the usual flow, because once you see it as a sequence, it becomes much easier to improve or adopt.
Most clinics rely on a mix of automation and human judgment.
A good policy spells out the categories. For example, high confidence matches that can move into a review queue, and lower confidence pairs that need more data first.
Nothing is merged yet. This is the verification stage, where your policy should be very explicit.
Typical checks include:
You want enough structure to avoid arguments, but not such a rigid script that staff cannot use their own judgment when the data is messy.
Next comes approval. Here is where many clinics drift into unwritten custom. The policy should answer three plain questions.
Most organizations require a reason, the specific records involved, date and time, and the user IDs of the people who made the call. That level of traceability matters if a clinician later asks why a chart looks different, or if a payer questions a claim.
When a merge is approved, one record becomes the surviving identity. Your MPI rules decide what happens when fields conflict, and how to keep historical encounters intact. The Merge Unmerge Policy should align with those rules and state, in ordinary language, what staff can expect.
This is where unified systems gain real value. If you use an AI powered unified inbox such as the one described on the solutions page for Solum Health, a clean MPI lets the platform keep all calls, texts, portal messages, and intake forms tied to one identity instead of many half complete profiles.
Unmerges are rarer, and they are more fragile. Once clinical notes, orders, and claims have gone out under a merged identity, you cannot simply peel the records apart and walk away.
A careful policy will specify:
The point is not perfection, the point is controlled repair instead of improvised rescue work.
Finally, every effective Merge Unmerge Policy includes basic oversight.
When you see patterns, you can adjust training or upstream workflows. That might mean revisiting your intake scripts, or pointing your AI intake automation, described in the Solum Health how it works flow, at the most error prone handoffs first.
If you do not have a formal policy, or if yours lives only in informal emails, here is a concrete path you can take in the next few weeks.
Talk to front desk staff, billing, and anyone who works denials or EHR clean up. Ask them how they currently handle duplicates. Write down the real process, not the ideal version.
For merges, agree on the minimum data set required before a merge can be approved. For unmerges, define a higher bar. This can be a short one page checklist.
Decide who can initiate a merge, who can finalize it, and who must be consulted for unmerges. Align that with your EHR and MPI permission structure so the policy matches reality.
Turn the policy into a brief reference that fits beside a monitor. Direct staff to deeper material, such as related entries in your internal or public glossary, when they want context.
If you are investing in a unified inbox, AI intake, or EHR inbox integration, tie those projects to the MPI discussion. A clean identity layer will increase the return on that automation spend.
From interviews and national reports, a few patterns recur.
First, trying to fix everything in one historic sweep, then letting the MPI drift again. A once per decade clean up does not help if staff create new duplicates every week.
Second, leaving the policy in the hands of only IT. This is really about access, throughput, and staff workload, so operations leaders and clinical leadership deserve a direct say.
Third, ignoring external guidance. Federal resources on identity management, from agencies such as the Office of the National Coordinator for Health Information Technology and the Agency for Healthcare Research and Quality, offer practical principles on patient matching, standard fields, and safety culture. It is worth aligning your policy with that broader direction.
Fourth, letting automation run without guardrails. AI or rules based matching can do a great deal of work, yet your policy should still carve out situations where a human must review the decision.
It is a written set of rules that governs how your organization identifies potential duplicate patient records, when it merges them into a single identity, when it separates them again, and how every action is logged.
Because those settings live on tight margins and high visit volume, even a modest duplicate rate can clog schedules, slow pre visit workflows, and increase rework for staff. A clear policy supports cleaner intake, more reliable communication, and steadier cash flow.
Ownership should be shared. Health information management, operations, and revenue cycle all have a stake, and IT must support the technical side. In practices that partner with an AI powered unified inbox like Solum Health, it is wise to involve whoever leads your automation and integration projects as well.
Vendor guidance is useful, but it does not know your local workflows, staffing model, or risk tolerance. You still need a local policy that translates vendor features into clear expectations for your own staff.
Most clinics benefit from a light review at least once a year, or any time you add new channels, such as a patient portal or SMS intake, or new automation that interacts with your MPI. When your communication footprint changes, your identity rules often need a tune up.
If you have read this far, you likely suspect that your MPI is carrying more risk and wasted effort than you would like. Here is a short action plan.
The work is not glamorous, but it is the kind that quietly improves access, protects staff capacity, and sets your automation projects up to succeed.
