KAHER Students: Health Records Analytics Internship Checklist for Medical and Allied Students

Yes, a medical or allied health student can complete a health records analytics internship without any programming background, provided they work methodically: check the data’s ethics status first, confirm the coding standards used, then compute a small set of well-defined outcome metrics. This checklist turns that sequence into a 14-day plan with a sample KPI table you can adapt.

Why health students have an advantage in clinical data work

Clinical data is messy in ways that only people with bedside or lab knowledge notice. A haemoglobin of 1.2 g/dL is probably a unit or typing error; a discharge date earlier than the admission date is a record-keeping fault; a diagnosis of “DM” could mean diabetes mellitus or something else depending on the department. A computer science student can write the code, but you can tell when the numbers do not make clinical sense.

What you may lack is a structured routine for the data side: how fields are defined, how to count things consistently, and how to protect patient identity. This checklist supplies that routine. It assumes you will use a spreadsheet (Excel or Google Sheets) as your main tool; anything more advanced is optional.

Before you touch any data: the ethics and privacy checklist

Do this part first, every time. Real patient records are sensitive, and an analytics exercise does not make them less so. For a short online internship, the safest route is almost always a public, synthetic or fully de-identified dataset.

• Know the data’s origin. Is it public, synthetic, or from a hospital? If it is from a hospital, you need written permission from the people responsible for it, and you should not take it into an online internship on your own.
• Never use real patient data from your clinical postings for an internship project unless your institution’s ethics process has explicitly allowed it in writing.
• Remove direct identifiers: name, full address, phone number, email, hospital or MRN number, Aadhaar or other ID numbers, photographs.
• Reduce indirect identifiers: convert exact date of birth to age or age band, exact dates to day numbers or months, and PIN codes to district or region. Rare conditions in small groups can identify a person even without a name.
• Suppress small counts. If a cell in a table shows fewer than five patients, merge categories or report “fewer than 5”.
• Store files sensibly. No sharing on messaging apps, no personal email attachments, no screenshots of raw rows in public posts.
• Record your assumptions. Keep a one-page note stating where the data came from, what you removed and who permitted its use.

India’s legal framework for personal data has been changing, so do not rely on memory or this article for the current legal position. If a project involves real patient information, ask your institution’s ethics committee or the data owner what is required.

Coding standards you should recognise in a clinical dataset

Clinical datasets usually carry standardised codes. You do not need to memorise them, but you must know what each one is for and check that a column uses a single system consistently.

The most useful habit is to build a data dictionary: a table listing every column, its meaning, its type (number, date, category, text), its allowed values and its unit. If the dataset lacks one, writing it is a legitimate and impressive deliverable.

Data quality checks to run on every clinical spreadsheet

1. Count rows and unique patients. One patient may appear on several rows (multiple visits). Decide whether your unit is the patient, the admission or the visit, and state it.
2. Look for duplicates. Use a combination of fields, not just one ID.
3. Check missing values column by column. Report the percentage missing before analysis.
4. Check plausibility. Age between 0 and about 110, systolic BP above diastolic, dates in the correct order, lab values in physiologically possible ranges.
5. Standardise categories. “M”, “male” and “Male ” (with a trailing space) must become one value.
6. Check units. Glucose, creatinine and haemoglobin often appear in mixed units.
7. Keep the original file untouched. Work on a copy and log each cleaning step.

Basic outcome metrics every non-CS health student should be able to compute

Start with simple, well-defined measures. Define each one in writing (numerator, denominator, time period) before calculating.

• Proportion or rate: number with an outcome divided by number at risk, for a stated period.
• Average length of stay (ALOS): total inpatient days divided by number of discharges.
• Readmission rate: readmissions within a stated window (for example 30 days) divided by index discharges.
• Mortality rate: deaths divided by admissions, ideally broken down by age band or diagnosis group because crude figures mislead.
• Median and interquartile range for skewed values such as length of stay or waiting time.
• Completeness rate: records with a required field filled divided by total records. This is a data-quality KPI worth reporting.

Sample KPI table for a clinical dataset (illustrative)

The figures below are invented to show layout and wording. They do not describe any real hospital.

Worked illustration: from a raw diabetes clinic sheet to three reportable KPIs

This is an illustrative scenario, not a real dataset, and no software run is claimed. Imagine a de-identified sheet of 400 visits by 250 patients to a diabetes clinic, with columns for age band, sex, visit date, HbA1c, ICD-10 code and follow-up attended.

1. Unit of analysis: you decide the patient is the unit for control, and the visit is the unit for attendance. You write this in your notes.
2. Cleaning: you find 12 duplicate visit rows and remove them, leaving 388. HbA1c has values entered as both percentages and mmol/mol; you convert them to one unit and log it.
3. Missing data: HbA1c is missing for 31 of 388 visits, which is about 8%. You report this openly.
4. KPI 1, control: using each patient’s latest HbA1c, count patients below your stated target, divided by patients with a value.
5. KPI 2, attendance: scheduled reviews attended divided by reviews scheduled.
6. KPI 3, completeness: visits with a valid ICD-10 code divided by all visits.
7. Interpretation: you add that the results describe this clinic sheet only and cannot show whether care is good or poor without comparison and case-mix adjustment.

KAHER students: fitting a health-data internship into a health-sciences setting

KAHER describes itself as a Deemed-to-be-University in Belagavi sponsored by the KLE Society, and its homepage lists constituent colleges including JN Medical College, colleges of pharmacy, nursing, physiotherapy, Ayurveda and homoeopathy, JGMM Medical College Hubballi and the School of Allied Health Sciences. The same page states a focus on health science education and research, and its course menu shows no computer science, IT or data science programmes. For you, that means analytics is a skill you add alongside your clinical training rather than something your degree is likely to teach in depth, so a structured, self-paced checklist like this one fills a real gap.

Your site also lists separate pages for Examinations, including a Dissertation/Thesis page and a Standard Operating Procedure page, as well as Courses and Admissions. These could be relevant if you plan to use data skills in a dissertation, but their contents have not been verified here, and internship or project-work rules may be set by the university or by individual colleges. The free two-week online internship from CareerSwami is independent of KAHER, so treat it as extra learning and check what your college expects before you list it anywhere official.

Verification steps for KAHER students before you start

1. Open the Examinations menu on the KAHER website and read the Dissertation/Thesis and Standard Operating Procedure pages for any rules on data use, ethics approval and external training.
2. Ask your department office in writing whether an online internship needs prior permission, and who signs it off.
3. Ask your institutional ethics committee whether secondary analysis of de-identified hospital data needs review, and obtain the answer in writing.
4. Confirm with your college whether any hospital dataset may leave the premises; if not, use only public or synthetic data.
5. Check the eligibility and duration page and your academic calendar for your own term dates so the 14 days do not clash with postings or exams.

14-day plan for a health records analytics internship

Final submission checklist for a clinical analytics report

• Data source and permission stated in one line.
• No direct identifiers anywhere, including chart labels and file names.
• Data dictionary attached.
• Each KPI has a numerator, denominator and period.
• Missing data percentages reported.
• Small counts suppressed or merged.
• Charts have titles, axis labels and units.
• Limitations section states that associations are not proof of cause.

Frequently Asked Questions

Do I need to learn Python or R for a health records analytics internship?

No. A spreadsheet with pivot tables and basic formulas covers all the metrics in this checklist. Programming helps with larger files and repeatable analysis, but it is optional at this stage.

Can I use patient records from my clinical rotation for the project?

Not without explicit written permission from the data owner and, where applicable, your ethics committee. Removing the name alone does not make a record anonymous. The safer choice is a public or synthetic dataset.

Which coding standard should I learn first?

ICD-10 is the most common starting point because diagnoses appear in nearly every clinical dataset. Learn to check the code format and consistency before worrying about SNOMED CT or LOINC.

What is the difference between a mean and a median length of stay?

The mean is total days divided by discharges and is pulled upward by a few very long stays. The median is the middle value and better reflects a typical patient. Reporting both gives a fuller picture.

How do I handle missing values in a clinical dataset?

First measure them by column and report the percentage. Then decide, and document, whether to exclude those rows from a specific calculation. Avoid filling gaps with guessed values in a short project.

Why is a crude mortality rate risky to compare between hospitals?

Hospitals treat different mixes of patients by age, severity and diagnosis. Without adjusting for these, a referral centre can look worse than a smaller unit purely because it treats sicker patients.

Does the 14-day internship offer something suited to this checklist?

The free two-week online internship offers 26 tracks, including a data analytics track, along with a free offer letter and a completion certificate after successful completion. Use this checklist to structure whichever dataset you work on.

What should I put in my CV after a health analytics project?

List the dataset type, the methods (cleaning, KPI definition, charts) and the specific outputs, such as a data dictionary and KPI table. Describe it accurately as a learning project and avoid implying clinical or institutional endorsement.

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