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This Health IT Module is compliant with the ONC Certification Criteria for Health IT and has been certified by an ONC-ACB in accordance with the applicable certification criteria adopted by the Secretary of Health and Human Services. This certification does not represent an endorsement by the U.S. Department of Health and Human Services.

Certification Detail

Intervention Risk Management for Predictive Decision Support Intervention

1. Diagnosis Selection



This feature is used to highlight the diagnosis from the Assessment and Plan, Chief Complaints sections in OMS EHR. We are using Named Entity Recognition, also called NER or Token Classification. The algorithm will predict or classify every word or token in the text into labels provided. Currently we are using only one label that is “Diagnosis”. This algorithm can be further enhanced by including other labels like ‘Medications’, ‘Allergies’ etc.,

Before the deployment of models in production, we are going through multiple steps to make sure that the efficient model is in place. We perform model evaluation right after training with unseen data to get a good estimate of how well the model generalizes in production. We then pass through unit testing to make sure that there is no leakage in code. After that, we pass through a set of tests to make sure that the model is responsible meaning it should be biased towards features like Race, Ethnicity etc., We are using a tool called LangTest to provide us scores for various metrics.

The best performing model will be selected, and it will be moved to production. Once it is in production, we need to monitor the model and the infrastructure where it is deployed. A scheduler will be in place where every certain interval (say every month), we will run a monitoring pipeline that will fetch the data from production and then compares it with the reference data, to make sure that there is no ‘Drift’ – both model drift as well as data drift. We will generate reports to make sure that the drift is not more than a threshold. If we find that the drift is high, we will run from data collection to model training and evaluation again to mitigate the drift and make the model perform well in production. This cycle will be repeated at a certain interval.

We will keep a track of model and data lineage to make sure to accurately reflect these changes over time. We will also make sure that the infrastructure where the model has been deployed will be monitored for to scale when needed during peak times.

2. Search



This feature is used to retrieve documents by specifying a keyword or set of keywords in the text of the documents. The user can enter a keyword to retrieve the documents that contain the corresponding keyword. By default, this module will search for any matching prefix that is entered by the user. The user can also restrict the prefix matching with exact matching by specifying the keyword in single quotes. The user can also search for documents that match multiple keywords entered by the user using the plus operator.

These are the steps that are used to make search work. We will select certain documents, extract the contents of the document and save it in DB. Then we will use specialized queries like DSL (Domain Specific Language) to retrieve the documents based on the matching. Only the documents that have a high chance of matching will be shown to the user, not all ones. We also check for any abbreviations related to medical domain being searched by the physician. If present, the system will check for both abbreviated as well as the original word.

The matched documents will highlight the text that has been searched for. The system is purely a brute force match (prefix-match and exact match) one.

Price Transparency

1. Name and version of the product: OMS EHR Version 5.1.0.00
2. Objective Medical Systems, LLC (OMS) agrees to notify Drummond Group of any and all future changes to our price transparency language for this certified product-version.
3. Objective Medical Systems, LLC (OMS) will charge customers an ongoing monthly recurring fee for availing the services of the Drug Database. The drug database is mandatory to attest to the following meaningful use measures: e-Prescribing (170.315.b.3), CPOE for Medications (170.315.a.1) and Drug-Drug / Drug- Allergy Interaction checks (170.315.a.4).
4. Objective Medical Systems, LLC (OMS) will charge customers an ongoing monthly recurring fee for supporting the e-prescribing functionality (170.315.b.3)
5. Objective Medical Systems, LLC (OMS) will charge customers an ongoing monthly recurring fee per provider for availing the services of CPT Codes, ICD-9 and ICD-10. This functionality is required to attest to the following meaningful use measures: CPOE (170.315.a.1), Clinical Information Reconciliation (170.315.b.2), Decision Support Interventions (170.315.b.11), CQM – Record and Export (170.315.c.1), CQM – Import and Calculate (170.315.c.2), CQM – Electronic Submission (170.315.c.3).
6. Objective Medical Systems, LLC (OMS) will charge customers an ongoing monthly recurring fee for supporting the “Direct” services for Transition of care – receive, display, incorporate (170.315.b.1) and Clinical Information Reconciliation and Incorporate (170.315.b.2).

Disclosures

OMS offers a monthly per provider lease fee model of its OMS EHR Version 5.1.0.00 software and also offers an annual per provider license fee model for the purchase of the OMS EHR Version 5.1.0.00. There is no annual maintenance for the lease model; an annual maintenance fee is charged on the license fee model to provide for support and maintenance of the software.