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A consistent naming pattern makes your logic easier to write, read, and debug.",[592,680,682],{"id":681},"plan-your-data-model","Plan Your Data Model",[597,684,685],{},"Before you create your first question, map out the full data model:",[618,687,688,694,700],{},[621,689,690,693],{},[663,691,692],{},"Inputs"," -- What data do you need to collect? List every question, measurement, and selection the user provides.",[621,695,696,699],{},[663,697,698],{},"Outputs"," -- What results do you need to produce? Define every score, classification, and recommendation the evaluation generates.",[621,701,702,705],{},[663,703,704],{},"Transformations"," -- How do inputs become outputs? Identify the calculations, lookups, and decision rules that connect them.",[597,707,708],{},"This planning step helps you catch missing data points before you build, saving you from restructuring later.",[592,710,712],{"id":711},"consider-units-and-constraints","Consider Units and Constraints",[597,714,715],{},"When you collect numerical data, define the units of measure and set appropriate constraints:",[618,717,718,721,724],{},[621,719,720],{},"Specify whether a weight field expects pounds or kilograms.",[621,722,723],{},"Set minimum and maximum values to prevent data entry errors (e.g., a heart rate between 30 and 250 bpm).",[621,725,726],{},"Determine decimal precision for measurements that require it.",[597,728,729],{},"Clear units and constraints protect the integrity of your calculations and prevent users from entering values that would produce meaningless results.",[592,731,733],{"id":732},"organize-sections-to-match-clinical-workflow","Organize Sections to Match Clinical Workflow",[597,735,736],{},"Arrange your sections in an order that mirrors the clinical evaluation process. Group related questions together so users experience a natural progression:",[618,738,739,742,745],{},[621,740,741],{},"Place demographic and intake questions first.",[621,743,744],{},"Follow with assessment and observation questions.",[621,746,747],{},"Position scoring and result sections at the end.",[597,749,750],{},"Logical section ordering reduces cognitive load for users and helps them complete evaluations efficiently without jumping back and forth.",[592,752,754],{"id":753},"keep-data-traceable","Keep Data Traceable",[597,756,757],{},"Maintain a clear chain from input through calculation to output. For every result your evaluation produces, you should be able to trace backward through the logic to the specific inputs that influenced it. This traceability is essential for:",[618,759,760,766,772],{},[621,761,762,765],{},[663,763,764],{},"Debugging"," -- When a result looks wrong, you can follow the chain to find where the error occurs.",[621,767,768,771],{},[663,769,770],{},"Validation"," -- When you create test scenarios, you verify that specific inputs produce expected outputs.",[621,773,774,777],{},[663,775,776],{},"Peer review"," -- When a colleague reviews your evaluation, they can follow the data flow and confirm its accuracy.",[597,779,780],{},"Build your data structure with traceability in mind from the start, and you will save significant time during testing and review.",{"title":782,"searchDepth":783,"depth":783,"links":784},"",2,[785,786,787,788,789,790,791,792],{"id":594,"depth":783,"text":595},{"id":602,"depth":783,"text":603},{"id":612,"depth":783,"text":613},{"id":653,"depth":783,"text":654},{"id":681,"depth":783,"text":682},{"id":711,"depth":783,"text":712},{"id":732,"depth":783,"text":733},{"id":753,"depth":783,"text":754},"Manage data integrity, labeling, and structure for your clinical evaluations.","md",null,{},true,{"title":390,"description":793},"TievXVOAddHFuGzZR2GNMm_C73KFo8kNK7ltkqr7E70",[801,803],{"title":386,"path":387,"stem":388,"description":802,"children":-1},"Define the problem, identify your users, and plan your clinical evaluation before building.",{"title":394,"path":395,"stem":396,"description":804,"children":-1},"Implement business rules, scoring algorithms, and test scenarios for clinical decision support.",1776705235185]