Doctor Skill Intelligence System
A structured framework for capturing, learning from, and operationalising each doctor's clinical decision-making — converting raw patient data into a living, physician-specific knowledge graph.
What problem are we solving?
Most hospital systems store the outputs of clinical work — symptoms, diagnoses, prescriptions — but never capture how doctors reason. This gap creates systemic failures in care quality and continuity.
No care standardisation
Identical presentations receive different treatment paths with no structured basis for comparison.
Invisible expertise
Senior physician decision patterns cannot be analysed, taught, or transferred to peers.
No predictive layer
Decision support tools lack physician-specific context to generate meaningful suggestions.
Data without intelligence
Existing records are archives — they record what happened but never why.
Our goal: Convert raw clinical data into a Doctor Skill Intelligence Layer — a structured, continuously-learning model of how each physician practices medicine.
Core idea
Every patient consultation is decomposed into a structured decision flow. Repeated across hundreds of encounters, this produces a data-driven profile of clinical expertise.
Get started
Quickstart Guide
Set up your first doctor skill profile in under 15 minutes.
→Specification
Complete technical specification for the data model and graph schema.
→For Administrators
Department-level comparison, skill transfer and protocol derivation.
→API Reference
Full REST API for reading the graph and generating ranked suggestions.
→System Specification
The complete format specification for the Doctor Skill Intelligence data model — covering node types, edge semantics, and the rules that govern how clinical knowledge is stored and retrieved.
What the specification defines
At its core, the DoctorAssist.ai specification defines a structured way to represent clinical knowledge as a property graph. Every consultation, decision, and outcome is encoded as a set of typed nodes and semantically meaningful edges, rather than a flat row in a table. This makes the data traversable, aggregatable, and learnable.
The specification covers five things: how node types are defined and what properties each must carry, how edges encode clinical reasoning with typed relationships and weight attributes, how threshold statuses are assigned and updated over time, how physician profiles are aggregated from raw encounter data, and how skill bundles are exported and imported across physicians.
The five node types
Edge semantics
Edges are not generic connections — each relationship type carries a defined meaning. The HAS_SKILL edge links a Doctor to a Condition and carries an expertise weight between 0 and 1. The OBSERVED edge links a Condition to a specific Observation snapshot. The LEADS_TO edge connects an Observation to an Action and carries a confidence score — this is the edge that encodes the physician's actual decision. The PRODUCES edge links an Action to an Outcome. Finally, the UPDATES edge closes the loop, linking an Outcome back to an Action with a delta value that adjusts the action's composite score incrementally after each encounter.
Key principle: The specification requires that every edge carries typed, non-null attributes. Generic or unweighted edges are not permitted — they carry no learning signal.
Required node properties
| Node Type | Required Properties |
|---|---|
| Doctor | id, specialty, skill_score |
| Condition | icd_code, label, frequency_count |
| Observation | id, encounter_id, timestamp, vitals (object) |
| Action | id, type, drug_or_procedure, confidence, success_rate, sample_count, threshold_status |
| Outcome | id, result, recovery_days, feedback_score |
System Architecture
How the Doctor Skill Intelligence System is structured — from raw clinical data ingestion through to real-time suggestion delivery at the point of consultation.
Three-layer design
The architecture is designed in three distinct layers that each serve a purpose: the ingestion layer collects and normalises raw clinical data, the intelligence layer converts that data into a structured knowledge graph, and the delivery layer makes the graph's insights available to clinicians and administrators in real time.
- 1
Ingestion layer
Structured clinical data — symptoms, diagnoses, investigations, prescriptions, and outcomes — is continuously pulled from existing hospital information systems. The ingestion layer normalises this data, resolves ICD codes, and creates encounter objects ready for graph conversion.
- 2
Graph intelligence layer
Each encounter object is parsed into nodes and edges and written to a graph database. Threshold computation runs after each new encounter write, updating confidence scores and reclassifying action nodes as ACTIVE, LEARNING, or WEAK. Physician skill profiles are derived continuously rather than on a batch schedule.
- 3
Delivery layer
A REST API exposes graph traversal results as ranked suggestion lists, physician profiles, department comparison reports, and exportable skill bundles. The API is the single integration point for clinical interfaces, mobile apps, and administrative dashboards.
Data flow
No workflow change required. The system reads from existing structured data sources. Physicians do not need to alter how they record consultations for the graph to learn from them.
Quickstart Guide
Get your Doctor Skill profile set up and your first decision flow captured. This guide walks you through the four steps needed to go from zero to a live skill profile.
Before you begin
Your hospital information system must already be connected to DoctorAssist.ai by your administrator. You do not need to install anything or change how you record consultations — the system reads from your existing structured data. Contact your department administrator if the integration has not yet been configured.
Steps to get started
- 1
Verify your physician profile exists
Log in to the DoctorAssist.ai portal and confirm your Doctor node has been created. Your specialty, department, and physician ID should be pre-populated from the HIS. If anything is missing, update it in the Profile Settings screen.
- 2
Review your initial skill graph
If you have six or more months of structured consultation data available in the system, your initial skill graph will already have been generated. Navigate to My Skill Profile to see the conditions, observations, and actions the system has learned from your history so far.
- 3
Understand your threshold statuses
Each action in your profile carries a status — ACTIVE, LEARNING, or WEAK — based on how often you have applied it and how often it led to a positive outcome. Actions marked ACTIVE will be surfaced to you during consultations. Review your WEAK actions and consider whether they reflect accurate historical decisions.
- 4
Enable consultation suggestions
In your account settings, enable the Live Suggestions toggle. From your next consultation onwards, DoctorAssist.ai will query your skill graph when a new patient is presented and return a ranked list of relevant actions based on the presenting condition and vitals.
Your profile improves automatically. Every consultation you complete updates your graph. Threshold scores are recomputed in real time — no manual input is required.
Skill Profile Setup
Your Skill Profile is the structured representation of how you practice medicine. This page explains what it contains, how it is built, and how to configure it correctly from the start.
What a skill profile contains
A Skill Profile is a physician-specific subgraph within the DoctorAssist.ai knowledge graph. It contains every condition you have treated, every clinical observation pattern associated with those conditions, every action (prescription, investigation, procedure) you have taken in response, and the outcomes that resulted. Each element is weighted by frequency, success rate, and outcome quality, giving the system a ranked, probabilistic view of your clinical preferences.
Profile configuration options
Specialty scope
Define which clinical domains your profile should cover. Multi-specialty physicians can configure separate skill graphs per domain.
Historical data window
Set how far back the system should look when building your initial profile — 3 months, 6 months, or all available history.
Threshold sensitivity
Adjust the minimum case count required for ACTIVE classification. Default is 10 — increase for higher confidence requirements.
Suggestion visibility
Control whether LEARNING-status actions are shown alongside ACTIVE ones, or suppressed until they reach full confidence.
Decision Flow Capture
How DoctorAssist.ai converts a clinical encounter into a structured decision flow — and what that means for your growing skill profile.
What gets captured
Every patient consultation you complete is parsed into a structured decision flow automatically, without requiring any additional documentation on your part. The system reads the presenting complaint, the vitals recorded at triage, the diagnosis you assigned, every investigation you ordered, every prescription or intervention you applied, and the outcome recorded at discharge or follow-up.
These elements are linked together as a chain: presenting condition → observed vitals → diagnosis → investigations → treatment → outcome. This chain is then written to your skill graph as a series of weighted edges, each one carrying a confidence value derived from the specificity of the condition context under which the decision was made.
A worked example
A patient presents with chest pain and a blood pressure reading of 150/90. You suspect angina, order an ECG and troponin, and prescribe aspirin 300mg. The patient is discharged after 48 hours with a recovered status and a feedback score of 0.91. The system writes five nodes — Condition, Observation, two Actions, and an Outcome — linked by four edges, each carrying the context under which that decision was made.
Why context matters
The system does not simply record that you prescribed aspirin. It records that you prescribed aspirin when the patient presented with chest pain and their blood pressure was above 140. This conditional specificity is what separates a learning skill graph from a simple prescription log. Over time, patterns like this accumulate into decision rules — not because anyone programmed them, but because the graph learned them from your actual practice.
Reading Your Profile
How to interpret the data in your Skill Profile — what each metric means, and how to use your profile to understand your own clinical patterns.
Skill score
Your overall skill score is a single number between 0 and 1 that reflects the weighted average of all ACTIVE action scores across your profile. It is not a measure of clinical ability — it is a measure of how consistently and successfully your recorded decisions have led to positive outcomes within this system. A score of 0.85 means that across all conditions where you have sufficient evidence, your decisions have a high and consistent rate of producing favourable outcomes.
Per-condition breakdown
The profile view shows a ranked list of conditions you have treated, sorted by the number of encounters. For each condition, you can see your preferred actions — the decisions you make most frequently under that condition — along with their individual confidence scores and threshold statuses. This gives you a structured view of your clinical tendencies that would otherwise only exist as implicit memory.
Understanding threshold statuses
| Status | What it means | Shown in suggestions? |
|---|---|---|
| ACTIVE | ≥75% success rate across ≥10 cases. High confidence decision. | Yes — top of ranked list |
| LEARNING | 50–74% success, or fewer than 10 cases recorded. | Optional — configurable |
| WEAK | Below 50% success rate. Low confidence — excluded by default. | No — hidden from suggestions |
Outcome Feedback
How outcomes from your consultations flow back into your skill graph — and why closing the loop is what makes the system learn.
How outcome data is used
Every time a patient is discharged or a follow-up is completed, the outcome — whether the patient recovered, showed no change, or experienced a complication — is written to the graph as an Outcome node. This node is then connected back to every Action node from that consultation via an UPDATES edge. The delta carried on that edge adjusts the composite score of each action upward or downward depending on whether the outcome was favourable.
This feedback loop is what transforms the graph from a static record into a self-improving model. Actions that consistently produce positive outcomes see their confidence scores rise over time. Actions that correlate with poor outcomes see theirs fall — and if they fall below the threshold, they are reclassified from ACTIVE to LEARNING or WEAK, and removed from suggestions accordingly.
What counts as a positive outcome
The system defines a positive outcome as a combination of two signals: a clinical result of recovered or significantly improved, and an outcome feedback score above 0.70. Both signals are weighted — clinical result accounts for 70% of the outcome score, and patient feedback accounts for 30%. This weighting reflects the primacy of clinical result while still incorporating patient-reported experience as a meaningful signal.
Department Comparison
Understand how decision patterns vary across physicians in your department — and use that insight to surface divergences, identify best practices, and inform training.
What the comparison view shows
The Department Comparison view lets administrators query the skill graphs of all physicians in a department simultaneously, filtered by a specific condition or ICD code. For each condition, the view returns a ranked table of physicians alongside their preferred actions for that condition — showing the action, confidence score, success rate, and threshold status for each.
This makes it immediately visible when two physicians treat the same presentation in fundamentally different ways. It also surfaces which physician's approach has the highest outcome-validated success rate — not based on seniority or anecdote, but on data from their actual encounters.
What you can do with this data
Identify top performers
See which physicians have the highest ACTIVE action success rates for specific conditions — grounded in outcome data, not perception.
Flag outlier decisions
Automatically surface cases where a physician's preferred action for a condition deviates significantly from peers with comparable experience.
Inform training programmes
Use comparison data to identify knowledge gaps and design targeted continuing education around conditions with low departmental success rates.
Validate new joiners
Track whether new physicians' decision patterns converge toward departmental norms over time, or diverge in ways that warrant intervention.
Protocol Standardisation
Derive evidence-backed institutional treatment protocols from the aggregated ACTIVE decisions of your most experienced physicians — replacing assumption-driven guidelines with outcome-validated standards.
How protocols are derived
A protocol is generated by querying the skill graphs of a selected group of physicians — typically your highest-scoring specialists for a given condition — and extracting the ACTIVE actions that appear consistently across all of them. An action qualifies for inclusion in a proposed protocol when it appears as ACTIVE in the profiles of at least 70% of the selected physicians and has a combined success rate above the institution-configured threshold.
The result is a ranked, condition-specific treatment pathway that reflects what your most effective physicians actually do — not what a textbook suggests they should do. These proposed protocols are surfaced to administrators for review before being ratified and pushed to the delivery layer.
Protocol lifecycle
- 1
Protocol proposed
The system generates a candidate protocol for a condition based on aggregated ACTIVE actions from selected physicians. The proposal includes the evidence base — which physicians, how many cases, and what success rates.
- 2
Clinical review
The proposed protocol is reviewed by the department head or a designated clinical committee. Any actions that are clinically inappropriate despite high data confidence can be excluded at this stage.
- 3
Ratified and published
Once approved, the protocol is marked as ratified and becomes available in the suggestion layer for all physicians in the department — not just those whose data contributed to it.
- 4
Continuous update
As new outcome data comes in, the system monitors whether ratified protocol actions maintain their success rate thresholds. If an action's rate falls significantly, the administrator is alerted to consider a protocol revision.
Skill Transfer
Export the validated clinical decision patterns of one physician and import them as a starting point into another physician's skill graph — with full provenance tracking.
What skill transfer does
Skill transfer allows an administrator to export all ACTIVE-status action nodes from a source physician's graph as a serialised bundle. This bundle is then imported into a target physician's graph — typically a new joiner or a physician moving into a new specialty area — where the imported actions are tagged with a TRANSFERRED_FROM edge that marks their origin.
Crucially, transferred skills do not arrive as ACTIVE in the receiving physician's graph. They are imported at LEARNING status, meaning they appear in suggestions at lower priority until the receiving physician accumulates their own outcome evidence for those actions. Once sufficient encounters confirm similar decision patterns and outcomes, the actions are promoted to ACTIVE under the receiving physician's own evidence — at which point the transfer attribution remains but is no longer the sole basis for the status.
Transfer rules
Borrowed skills never override local evidence. If a receiving physician's own encounter data produces a WEAK classification for a transferred action, that classification takes precedence over the transferred status — regardless of how strong the source physician's evidence was.
- 1
Select source physician
Choose the physician whose ACTIVE skills you want to transfer. The export includes only ACTIVE actions — LEARNING and WEAK actions are excluded.
- 2
Select condition scope
You may transfer the full graph or limit the transfer to specific conditions or ICD code ranges — useful when onboarding a specialist into a focused area.
- 3
Import into target physician
The bundle is imported into the target physician's graph. All imported actions carry TRANSFERRED_FROM provenance and start at LEARNING status.
Analytics Dashboard
A high-level view of clinical intelligence metrics across your institution — physician skill scores, department-level success rates, and condition coverage.
What the dashboard shows
The Analytics Dashboard aggregates data from all physician skill graphs in your institution and presents it at three levels: institution-wide summary metrics, department-level breakdowns, and individual physician performance cards. Each level can be filtered by time period, condition category, or physician cohort.
Key dashboard panels
Physician skill leaderboard
Ranked list of all physicians by overall skill score, with drill-down into per-condition performance.
Condition heat map
Visual map of conditions by treatment coverage — highlighting conditions where departmental ACTIVE action density is low.
Threshold promotion timeline
Trend chart showing how many actions have moved from LEARNING to ACTIVE over the selected period.
Outcome success rate trend
Institution-wide average outcome success rate over time — the primary indicator of whether clinical intelligence is improving care.
Graph Schema
The underlying data model — how every clinical encounter is stored as a connected graph of typed nodes and semantically meaningful edges.
Node structure
The underlying data model stores every clinical encounter as a connected graph rather than a flat record. Each physician is represented as a Doctor node carrying their specialty and an overall skill score. Conditions are stored as Condition nodes tagged with their ICD classification code and a frequency count that grows with each encounter. Observations — such as blood pressure readings, heart rate, and lab findings — are stored as Observation nodes linked directly to the condition they were captured under.
Each clinical action — a prescription, procedure, or investigation — becomes an Action node. This node holds not just the drug name and dosage, but also its computed confidence score, success rate, the number of times it has been applied, and its current threshold status. Outcomes are stored as separate Outcome nodes recording the result, recovery duration, and a patient feedback score.
Edge relationships
The relationships between nodes carry the clinical reasoning. A Doctor is connected to a Condition via a HAS_SKILL edge weighted by expertise level. A Condition connects to an Observation via OBSERVED. An Observation connects to an Action via a LEADS_TO edge that carries the confidence value — this is the edge that encodes the clinical decision. An Action connects to an Outcome via PRODUCES, and crucially, the Outcome feeds back into the Action via an UPDATES edge that carries a delta value, incrementally adjusting the action's composite score with every new encounter.
This feedback loop is what makes the graph a living model. Every outcome — positive or negative — adjusts the weights of the edges it is connected to, continuously refining the accuracy of the physician's skill representation.
Relationship summary
| Relationship | From → To | Key attribute |
|---|---|---|
| HAS_SKILL | Doctor → Condition | weight (0–1 expertise level) |
| OBSERVED | Condition → Observation | encounter_id, timestamp |
| LEADS_TO | Observation → Action | confidence (0–1) |
| PRODUCES | Action → Outcome | encounter_id |
| UPDATES | Outcome → Action | delta (positive or negative float) |
| TRANSFERRED_FROM | Action → Doctor | source_physician_id, transfer_date |
Threshold Computation
How confidence scores are computed for every action node — and how those scores determine whether an action is surfaced to a physician during consultation.
The composite score
Every action node in the graph carries a continuously-updated composite score that reflects how strongly a physician prefers a particular decision under a given set of conditions. This score is calculated by combining two signals: the clinical success rate of past decisions, weighted at 70%, and the average outcome feedback score from patients, weighted at 30%. Together, these produce a single number between 0 and 1 that represents the reliability and quality of a physician's decision pattern.
Threshold classification
Based on the composite score, each action is automatically classified into one of three threshold statuses. An action is marked ACTIVE when it has been applied in at least ten cases and has achieved a success rate of 75% or above — meaning the system is confident this is a strongly preferred and effective decision. It is marked LEARNING when evidence is still being gathered, either because fewer than ten cases have been recorded or the success rate sits between 50% and 74%. Actions that fall below 50% success are marked WEAK and are excluded from suggestions surfaced to the physician.
Continuous recomputation
Threshold scores are not computed on a batch schedule. Every time a new outcome is written to the graph, the UPDATES edge triggers a recomputation of the composite score for every Action node that outcome is connected to. This means a physician's profile is always current — a run of poor outcomes can demote an ACTIVE action to LEARNING within the same day, and a run of strong outcomes can promote a LEARNING action to ACTIVE just as quickly.
Status changes are always surfaced. Physicians and administrators receive a notification when any action in their profile changes threshold status — promotion or demotion — so the change is never silent.
API Reference
The REST API is the single integration point for all clinical interfaces, dashboards, and external systems. It exposes graph traversal results as structured, ranked responses.
Core endpoints
| Endpoint | Purpose |
|---|---|
| GET /suggestions | Returns a ranked list of ACTIVE actions for a given physician, condition ICD code, and observation snapshot. The primary endpoint used at consultation start. |
| GET /profile/{physician_id} | Returns the full skill profile for a physician — all conditions, actions, scores, and threshold statuses. |
| POST /outcome | Writes a new outcome node to the graph and triggers threshold recomputation for all connected action nodes. |
| GET /compare | Returns a side-by-side comparison of skill profiles for a list of physicians, filtered by condition. |
| POST /transfer | Initiates a skill transfer from one physician to another. Accepts source physician ID, target physician ID, and optional condition scope filter. |
| GET /protocol/{condition_icd} | Returns the ratified protocol for a condition, if one exists, as an ordered list of actions with evidence metadata. |
Authentication
All API requests require a Bearer token issued to your institution's DoctorAssist.ai integration. Tokens are scoped to a specific institution and cannot access data across institution boundaries. Physician-level tokens can only read their own profile — administrator tokens have read access across all physicians in their institution and write access to protocol management endpoints.
Data Models
The typed data structures behind every node and edge in the Doctor Skill Intelligence graph — including field names, types, and validation rules.
Doctor node model
The Doctor node is the root of each physician's skill graph. It carries a unique system-assigned identifier, the physician's registered specialty, and a continuously-computed overall skill score. The skill score is the weighted average of all ACTIVE action composite scores across the physician's full graph. It is updated whenever any action in the graph changes composite score.
| Field | Type | Description |
|---|---|---|
| id | string | Unique physician identifier, system-assigned at onboarding |
| specialty | string | Primary clinical specialty (e.g. cardiology, general medicine) |
| skill_score | float (0–1) | Weighted average of all ACTIVE action composite scores |
| created_at | timestamp | Date the physician's graph was initialised |
| last_updated | timestamp | Date of most recent skill score recomputation |
Action node model
The Action node is the most data-rich node in the graph, because it is the node that carries the learning signal. Every prescription, investigation, or procedure you order becomes an Action node with the following structure.
| Field | Type | Description |
|---|---|---|
| id | string | Unique action identifier |
| type | enum | prescription | investigation | procedure |
| drug_or_procedure | string | Name of drug, test, or procedure |
| confidence | float (0–1) | Current computed confidence score |
| success_rate | float (0–1) | Proportion of cases with a positive outcome |
| sample_count | integer | Total number of times this action has been applied |
| threshold_status | enum | ACTIVE | LEARNING | WEAK |
Integrations
How DoctorAssist.ai connects to your existing hospital information systems, EMRs, and clinical interfaces to ingest data and surface suggestions without disrupting your workflows.
Supported integration methods
HL7 FHIR R4
Native support for FHIR R4 resource types — Patient, Observation, MedicationRequest, DiagnosticReport, and Condition — for standards-compliant data ingestion.
Direct HIS connector
Pre-built connectors for major hospital information systems including Epic, Cerner, and Meditech — configured by your administrator at onboarding.
REST webhook
Configure a webhook endpoint on your HIS to push encounter data to the DoctorAssist.ai ingestion API in real time on discharge or encounter closure.
Batch CSV import
For institutions without a live integration, historical data can be ingested as structured CSV exports from your existing systems using our batch import template.
Minimum data requirements
For the graph to learn meaningfully, the integration must supply at minimum: a patient encounter identifier, a presenting condition or ICD code, at least one vital observation (blood pressure, heart rate, or temperature), at least one action (prescription, investigation, or procedure), and an outcome on discharge or follow-up. Encounters missing any of these five elements are ingested but flagged as incomplete and excluded from threshold computation until the missing element is supplied.
Clinical Case Studies
Real-world examples of how the Doctor Skill Intelligence System captures, learns from, and applies clinical decision patterns — illustrated through documented encounters.
Case 1 — Chest pain with hypertension
A 58-year-old male presents to a cardiology department with acute chest pain and a blood pressure reading of 150/90. The attending cardiologist suspects angina, orders an ECG and serum troponin, and on confirmation of ST elevation prescribes aspirin 300mg. The patient is discharged after 48 hours with a fully recovered status and a feedback score of 0.91.
The system writes this encounter as a chain: Condition node for chest pain linked to an Observation node carrying the BP and ECG findings, linked to two Action nodes — aspirin and the troponin investigation — each with a LEADS_TO edge carrying a confidence of 0.92. The Outcome node records the recovery and closes the loop via an UPDATES edge with a positive delta, incrementally increasing the composite scores of both action nodes.
After 44 encounters with similar presentations, this cardiologist's aspirin action node reaches a composite score of 0.87 and is classified ACTIVE. It now appears at the top of the suggestion list for every subsequent chest pain encounter where BP exceeds 140.
Case 2 — Skill transfer on department onboarding
A newly-joined general physician is assigned to a cardiology rotation. The department administrator exports the ACTIVE skill bundle of the department's most experienced cardiologist — covering 34 condition types and 112 action nodes — and imports it into the new physician's graph. All 112 action nodes arrive at LEARNING status with TRANSFERRED_FROM provenance. Over the following three months, as the new physician accumulates their own encounters, 78 of the transferred actions are confirmed by local evidence and promoted to ACTIVE. The remaining 34 remain at LEARNING until sufficient encounters are recorded.
Changelog
A record of significant updates to the Doctor Skill Intelligence System — new capabilities, model improvements, and API changes.
- v0.4
Skill Transfer and Provenance Tracking
Added the ability to export and import physician skill bundles. All transferred actions now carry a TRANSFERRED_FROM provenance edge and arrive at LEARNING status in the receiving graph. Administrator transfer UI available in the portal.
- v0.3
Department Comparison and Protocol Derivation
Released the Department Comparison view and the protocol derivation engine. Administrators can now query cross-physician skill graphs by condition and generate candidate protocols from aggregated ACTIVE actions.
- v0.2
Live Suggestion Engine
The API's /suggestions endpoint went live. Graph traversal now returns ranked action lists at consultation start, filtered to ACTIVE-status actions and ordered by composite score.
- v0.1
Graph Ingestion and Threshold Computation
Initial release. Clinical encounter data ingested from HL7 FHIR R4 sources. Graph schema established with five node types and five edge relationship types. Threshold recomputation running on every outcome write.
Frequently Asked Questions
Answers to the most common questions about how the Doctor Skill Intelligence System works, what data it uses, and how it affects clinical workflows.
- ?
Do I need to change how I document consultations?
No. DoctorAssist.ai reads from your existing structured clinical data. As long as your HIS captures the consultation elements the system needs — presenting condition, vitals, actions, and outcomes — nothing in your workflow changes.
- ?
How much historical data does the system need to generate a useful profile?
A minimum of 50 complete encounters across at least five conditions is recommended before the suggestion engine produces reliable results. With fewer encounters, profiles are generated but most actions will remain at LEARNING status with insufficient evidence to qualify as ACTIVE.
- ?
Can the suggestions override my clinical judgement?
No. Suggestions are ranked recommendations derived from your own decision history, not prescriptive instructions. The physician retains full clinical authority. The suggestion list is an aid, not a directive.
- ?
Who can see my skill profile?
By default, only you and your institution's designated administrators can view your profile. Administrators have read access for the purposes of department comparison and protocol derivation. Profile data is never shared outside your institution boundary.
- ?
What happens to my profile if I move to a different department?
Your graph moves with you. If you transfer to a new specialty, a new scope can be configured by your administrator, and the system will begin building condition coverage in the new area while preserving your existing profile from your previous specialty.
- ?
Is the system certified for clinical use?
DoctorAssist.ai is currently in private beta. Clinical certification and regulatory approval status varies by jurisdiction. Please contact your administrator or our team for the current certification status applicable to your region and institution type.
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