PackagePublished package

@pasar6987/sf-health-medication

Name: Semantic model package @pasar6987/sf-health-medication
Creator: pasar6987

Pharmaceutical care, reconciled. Handles medication reconciliation, therapy reviews, statement-level analysis, and the recommendations that help clinicians optimize drug regimens and catch interactions.

OSISchema 0.1.1Domain crm Source developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Published package · Latest published 1.0.1 Mar 4, 2026 · 6 datasets / 0 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

6 datasets0 measures · 74 dimensions

Semantic Graph

Relationship counts appear after the graph loads.

Loading graph

Package graph is loading

Package relationships will appear when the summary is ready.

Reference context

Secondary package facts stay compact

Updated: Mar 11, 2026
Visibility: Public hub listing
License: MIT
Created: Mar 4, 2026

Reference facts

Secondary package facts after the usage path is clear

Licensing, categorization, ownership, and linked metadata stay below the runnable path so the page reads in the right order.

Format

OSI

Upstream

developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Repository

Not linked

Domain

crm

Source

developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Owner

@pasar6987

Organization

Independent

License

MIT

Visibility

Public hub listing

Publisher

@pasar6987Published Mar 4, 2026

Latest published version

1.0.1Published Mar 4, 2026

Use this package in code and AI

After structure review, move straight into typed reads with load() or compact LLM context with to_prompt().

Python SDK

Python example for @pasar6987/sf-health-medication

This example uses the current package ref and, when preview data is available, fills in real dataset names from the published summary.

import rawctx

model = rawctx.load("@pasar6987/sf-health-medication")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-health-medication",
    datasets=["MedReconRecommendation", "MedReconStmtRecommendation"],
    max_tokens=2000,
)

print(model.datasets)        # ["MedReconRecommendation", "MedReconStmtRecommendation", "MedTherapyStmtReviewIssue"]
print(model.measures)        # [Measure(name="measure_a", ...)]
print(model.dimensions)      # [Dimension(name="Description", ...), Dimension(name="LastReferencedDate", ...), Dimension(name="LastViewedDate", ...)]
print(model.relationships)   # [Relationship(name='...', ...)]
print(prompt)

See Python docs

README

Package narrative and examples

Use documentation after the package clears provenance, ownership, and runnable-path checks.

@pasar6987/sf-health-medication

Overview

	Count
Objects (Datasets)	6
Dimensions	74
Measures	0
Relationships	0

Objects

MedReconRecommendation — Stores information about a medication recommendation and associates a recommendation to a medication reconciliation.
MedReconStmtRecommendation — Associates a medication recommendation to a medication statement and medication reconciliation.
MedTherapyStmtReviewIssue — Represents a junction object between the Medication Therapy Statement Review and Clinical Detected Issue objects.
MedicationReconciliation — Stores information about a medication reconciliation conducted for a patient
MedicationTherapyReview — Stores information about a medication therapy review conducted for a patient.
MedicationTherapyStmtReview — Represents a junction object between the Medication Therapy Review and Medication Statement objects.

Install

rawctx snapshot-download @pasar6987/sf-health-medication

Structure review

Inspect package structure after the usage path is clear

Use the structural review when you need the package footprint, field counts, and model paths before a deeper explorer pass.

Models1

datasets6

measures0

dimensions74

relationships0

AI context1

models/sf-health-medication.osi.yamlAI context included

6 datasets0 measures74 dimensions0 relationships