## Healthcare (clinical/administrative) spreadsheets

### Tab structure
- Use separate tabs for distinct data sets and separate raw data from summary or analysis: Data Entry (raw clinical or administrative data inputs), Calculations (where any metrics or aggregations are computed), and Reports (for summaries, charts, or tables for decision-making).
- In a clinical context, you might have one sheet for patient-level data (each row = one patient or encounter), and another sheet that pivots or summarizes by clinic or by month.
- Administrative workbooks (like a staffing or finance tracker) might split sheets by function – e.g., a “Staff List” sheet, a “Schedule” sheet, a “Payroll Calc” sheet, etc.
- Name each tab descriptively, such as “Appointments_RawData”, “Clinic KPI Dashboard”.

### Formatting
- Clarity and readability can have real consequences in healthcare, so adhere to common formatting signals.
- Highlight input fields (shaded light yellow) or form elements that staff are meant to fill out. If input is not used for calculation, placeholder “enter data here” may be used.
- Use conditional formatting to flag values that are out of normal range or require attention: e.g., automatically shade a cell red if a lab value is critical, or highlight an entire row if a patient is overdue for a follow-up.
- Include color legend (e.g. red = critical, orange = caution, green = within target, etc.).
- Use clear data formatting (e.g. YYYY-MM-DD or MM/DD/YYYY as required), identifiers (like medical record numbers), and units for all measurements (e.g. weight in kg, temperature in °C).
- Layout must be printable and scannable in urgent situations: use adequate font sizes, avoid crowding too much info, and freeze header rows for long lists of data so column titles (like “Patient ID”, “Last Visit Date”) are always visible.


### Formulas
- Accuracy is paramount, so double-check and simplify formulas wherever possible. If calculating clinical indicators (e.g. average length of stay, readmission rates), use straightforward aggregate formulas or pivot tables, which are less error-prone than manually crafted arrays.
- Where calculations involve thresholds or guidelines (say, flagging patients with BMI over a certain value), consider referencing those thresholds from a single cell named “BMI_threshold” so that it’s clear and adjustable, rather than embedding the number in multiple formulas.
- Include sanity checks: for example, if you calculate a total patient count from different categories, include a check that the sum of categories equals the total from the raw data. Any critical formula (like one computing a dosage or a financial total) might be independently verified or at least labeled clearly with comments.

### Raw data and outputs
- Never alter or delete original healthcare data
- For data entries that need correction (e.g., a misspelled diagnosis or an out-of-range age), consider doing it via a data validation process or noting corrections in a separate column (“Corrected value”) rather than overwriting, or maintain an audit log of changes.


### Metadata, units, and codes
- Healthcare data is full of codes and units, which must be documented.
- Always provide the units for each metric (e.g., blood glucose “mg/dL”, heart rate “bpm”).
- If the sheet is used for clinical purposes, including normal ranges or target ranges in the metadata can be helpful (for instance, note “Normal range: 70–100 mg/dL” next to a blood sugar metric).
- Make use of separate sheets to list code definitions: for example, a sheet “Codebook” could map ICD-10 codes or lab test codes to plain English descriptions.
- If the main data uses abbreviations (like “M”/“F” for sex or clinic codes like “NYC” for New York Clinic), ensure there is a legend or the full term in the header (“Sex (M/F)” is clear).
