Healthcare transitions and care decisions often require specific documents to be available and legally valid at the exact moment a clinical or administrative decision must be made.
When documents are missing, outdated, inaccessible, or legally mismatched with the patient present, staff and families may experience delays, confusion, unexpected expenses, insurance complications, and repeated administrative work.
In this repository, these events are treated as document-readiness failures.
These failures are rarely studied as a system problem, but they appear repeatedly during hospital admissions, assisted living transitions, and other healthcare decision points.
These problems are usually handled reactively. This project explores whether document readiness failures can be classified, tracked, and analyzed as operational events rather than treated as isolated paperwork problems.
The goal of this repository is to build a simple dataset structure that models document-readiness failure patterns and enables exploratory analysis of when and how they occur.
This repository contains only synthetic or illustrative data and does not include protected health information.
Healthcare systems routinely collect documents during intake, but those documents are often only tested operationally during care decisions or discharge planning.
This project studies document-readiness failures that emerge between those two stages.
This project uses three primary document failure categories.
A required document is not available at the moment it is needed.
Examples:
healthcare proxy not available at admission
DNR or MOLST form unavailable during urgent care decisions
identification or insurance documentation missing during intake
Operational impact may include delays, additional staff time, and unresolved decision authority.
A document exists but cannot be used effectively at the moment it is required.
Examples:
document stored at home while care occurs elsewhere
outdated advance directive
records stored in inaccessible systems
paper copies not available during care transitions
Operational impact often includes staff searching for documentation or requesting additional copies.
The individual present during a decision event may not have the legal authority required for the specific decision.
Examples:
family member present but not the legal healthcare proxy
financial authorization required but different authority holder
multiple family members unsure who has decision authority
Operational impact may include decision delays or additional verification steps.
The dataset models document-readiness events as rows in a table, where each row represents a single operational situation in which paperwork issues appeared.
Example fields include:
Field Description event_id Unique identifier for each event care_setting Setting where the issue occurred (hospital, rehab, hospice, assisted living, etc.) trigger_event Operational moment where the issue appeared (admission, discharge, transfer, consent, etc.) failure_type Category of document readiness failure document_type Type of document involved (POA, MOLST, insurance card, etc.) decision_urgency Urgency level of the situation authority_present Whether a decision-maker was present authority_verified Whether authority could be confirmed accessibility_issue Whether the document existed but could not be accessed staff_time_impact Estimated operational burden family_confusion_level Observed confusion level among participants downstream_delay Whether the issue caused a delay preventable_upstream Whether the issue appears preventable before the event notes Additional context about the situation
This structure allows exploration of patterns across different care settings and event types.
The observation files in the observations/ directory represent individual document-readiness events.
Observations are derived from publicly shared caregiver discussions and anecdotal reports. Personal details are removed to preserve privacy.
The first few observations currently contain synthetic example cases used to illustrate the structure of the dataset. These will be replaced or supplemented with real-world observations over time.
This dataset structure allows several types of exploratory questions.
Examples include:
Which failure type occurs most frequently?
Which document types appear most vulnerable to failure?
Which care settings experience the most document readiness problems?
Which failure types correlate with the highest staff time burden?
Which events most often lead to downstream delays?
Which scenarios create the highest family confusion levels?
Which document failures appear most preventable upstream?
Are certain trigger events predictable points where readiness failures emerge?
Do certain document types consistently cause problems during transitions?