8.1.0 CDC Opioid Prescribing Guidelines - Process Documentation

This section describes the functional behavior of the recommendations as implemented for the chosen pilot sites, and documents the process that was used to establish the functional behavior once the target recommendations were chosen.

8.1.1 Assumptions

Assumptions made in this process were based on team understanding of Epic and Cerner EHR capabilities, as well as team experience with clinical decision support (CDS) implementation in general, and opioid CDS specifically. Clinical Assumptions

Opioids for chronic pain management are often not started with an intent for chronic use; such prescriptions often transition from intended acute paitn management. Therefore, it is very difficult to identify a new opioid prescription a prior as for chronic pain management.

Indications and associated diagnoses for opioid prescriptions are often not available within the EHR.

End dates for opioid prescriptions are often not reliable. Technical Assumptions

The initial focus is on Epic and Cerner EHR platforms.

The technical framework to be used for EHR integration is CDS Hooks, with the FHIR Clinical Reasoning Module to represent the behavioral artifacts.

Widely scaling this type of CDS is still belleding-edge clinical informatics work, so an iterative and agile approach is recommended.

Both Epic and Cerner will be capable of supporting the provision of the following information:

  • Opioid medications about to be prescribed
  • Problem list, including for cancer
  • Prior uring drug testing results

The degree to which standard vocabularies are used by Epic and Cerner to provide this information must be verified at each implementation site, and to the extent that they are not uniform, terminology mappings will need to be established.

8.1.2 Decision Points Opioid Determination

The first issue to establish is how to determine what constitutes an opioid. The CDC guideline uses NDC codes derived from SAS calculations intended for use against prescription data. Prescription data can included ordered medication and/or dispensed medication, depending on availability at the local site.

To avoid excess alerting, the algorithm uses the following exclusions:

  1. Oral liquid medications, assuming the greatest majority of prescriptions for liquid oral formulations are intended for patients unable to swallow pills or tablets due to late stage terminal illness.
  2. In some cases, EHR vendors have developed approaches to identify MME equivalents. In such situations, the implementation should attempt to match the vendor's approach so that end users receive consistent advice.

For geographic locations with pharmacies that participate in Surescripts, implementation sites have the optio to use dispense data to perform the MME calculation. However, coverage varies by region, and even for regions with coverage, licensing costs may be required and must be considered as a factor when determining whether to use dispense data. Calculation of Daily Dosage

Calculation of daily dosage is based on the prescribed (order) or dispensed data based on expected use at the implementation site. The patient may be taking medication less often than prescribed or expected. For example, medication ordered every 4 hours as needed (PRN) leads to a calculation of six doses with a 24 hour period.

The daily dosage calculation is different based on whether dispsensed data is used:

Order Data (total daily dose based on the sig)

Expected use: Total daily dose = quantity per use * strength * daily frequency

Example: Order for two 24 mg tabs every 4 hours = 2 * 24 mg * 6 = 300 mg per day

Dispensed Data (dose not use sig)

Expected use: Total daily dose = (number dispensed * strength) / duration (days)

Example: Dispensed 60 pills, 25 mg each, for 30 days = (60 * 25) / 30, or 50 mg per day

Actual use may be less per day for a longer time period for either calculation, but the MME calculation addresses the prescribed or dispensed data. It is also possible that patients received dispensed medication and failed to ingest some or all of it. The extent to which differences exist between actual use and ordered or dispensed data requires access to reconciled medication data and may be confounded by trust issues.

The National Comittee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS) took a similar approach to determining cumulative medication use over time. The related electronice clinical quality measure (eCQM) CMS 128 addresses continuous use of anti-depressant medication management as 84 to 114 days (short term) and 180 to 231 days (long term) based on active medication and can use prescribed or dispensed data as they are available. The eCQM assumes the medication is dispensed and used as written. Determining Opioid Usage

Active medications in an EHR may have a start date and indicate the number of doses written; however, if not discontinued, the algorithm assumes the ordered medication remains active.

The logic assumes the prescription is filled on the day of the order.

If the prescription includes refills, the system assumes the patient requested all refills of the medication. Information to assist with Non-Medication Terminology Decisions

  • Value sets provided include concepts from standardized code systems (e.g., SNOMED-CT, ICD-10-CM, RxNorm) to indicate inclusion criteria for a given data element. However, clinicians often document using natural language and/or local codes. In addition, some sources only capture billing codes. The implementation site must have a mechanism to map such clinician-entered data to the content of the value set to determine if a patient's data represents the information required for processing the clinical decision support. Vendors and implementation sites may use third party vendors and tools to perform such mappings. The result is a need to create the mappings for each guideline valueset based on locally available information. To assure consistency of such translations across settings and vendor products requires careful study and analysis. The process also requires effort on the part of the EHR, local implementation site, or third party vendor; new value sets must be analyzed and individually configured in the system, and existing value sets must be updated regularly to support newly available codes, and reviewed for accuracy.
  • Some concepts seem relatively straigthforward when included in a clinical guideline for consumption by a clinician. However, when setting up queries to retrieve data from EHR or other clinical software data storage, the concepts needed may be difficult to encode using available code systems. Value sets can help with problem only in so far as the concepts are clearly defined without ambiguity. A good example is the definition of end-stage, stage 4 or terminal cancer, for exclusion from opiate usage calculation. One approach is to develop a value set indicating metastatic cancer and pancreatic cancer. This has the potential of covering the vast majority of cancers and secondary malignant neoplastic disease. Avoiding primary neoplastic and non-neoplastic disease seems prudent as these diagnoses are less likely to identify patients for whom the opportunity for additional treatment and quality of life has passed. However, successfully identifying patients for whom opiate medications should be ignored might better be managed by using encounter location or type to identify patients assigned to hospice care, e.g.
    • Procedures
      1. Referral to hospice (procedure) SCTID : 306205009
      2. Admission to hospice (procedure) SCTID: 305336008
      3. Urgent admission to hospice (procedure) SCTID: 183919006
      4. Discharge to healthcare facility for hospice care (procedure) SCTID: 428371000124100
    • Findings
      1. Full care by hospice (finding) SCTID: 170935008
      2. Transition from self-care to hospice (finding) SCTID: 448451000124101
      3. Transition from acute care to hospice (finding) SCTID: 1891000124102
      4. Transition from long-term care to hospice (finding) SCTID: 1951000124104
      5. Transition from long-term care to hospice (finding) SCTID: 1951000124104
    • Regime/therapy:
      1. Dying care (regime/therapy) SCTID: 385736008
    Because it is so difficult to identify individual patients who are appropriately receiving high opioid doses, it is important to allow the physician to override the alert with a reason, e.g., to indicate end-of-life care. Since the override option has the potential of over alerting, a mechanism to determine end-of-life care as provided using hospice care seem preferable.

8.1.3 Functional Description

The following two criteria must be satisfied for every patient for each recommendation:

  • Patient is being prescribed opioid medications for chronic pain
  • Patient does not appear to be at end of life

Chronic Pain

TODO - the decision points for this criteria are not well defined.

End of Life Assessment

The assessment to determine whether the patient appears to be at end of life consists of three elements:

  • Patient has been diagnosed with a condition that has a limited life expectancy prognostic estimation
  • Patient has been prescribed a medication that indicates the patient is approaching end of life
  • Patient has been referred, discharged or admitted to hospice care