Data Quality Can Make Or Break Your Healthcare Business

Whether it’s handwritten by a doctor on a sheet of paper or entered into a computer system, data plays a crucial role in healthcare. At an individual level, patient data determines the course of treatment. On a larger scale, aggregate data such as the number of patients with similar symptoms, demographics, number of patients admitted, etc. helps in the formulation of new medication and vaccinations.

The type and amount of data collected is vast but for it to make sense and be useful, it must be organized, accurate and up to date. This is where data quality comes in. Without it, all you’d have would be a bunch of names and numbers.

Accurate, Valid Data Determines Treatment Courses And Procedures

A doctor cannot treat a patient correctly unless he has access to the correct facts “ what are the symptoms, the patient’s age and particulars, his medical history, etc. If the original data being collected is not accurate, the wrong impression may be created. Instances where accuracy in data for healthcare is important includes:

· Recording of the patient’s identification details with correct spellings

· Relevant facts and vital signs about the issue at hand

· All details in the health record refer to the same person

· Codes used by medical organizations to classify procedures, diseases and forms of treatment

Reliable Data Minimizes The Risk Of Errors

Repeated collection of data and its processing should reveal reliable, consistent results. A simple mistake in noting down a patient’s age can affect the dosage of medication. Similarly, if the hospital record and the patient’s copy of medical records do not match, the doctor may find it difficult to ascertain the patient’s medical history. Some examples of reliability include:

· The name, gender and age of the patients is consistent across all medical records

· The diagnosis recorded on the hospital medical records is the same as the diagnosis noted on the progress notes and patient’s copy of medical records

Complete Data Records Ensure Holistic Treatment

All required data fields must be filled in for the doctor to have a complete picture. For example, if the blood pressure of a hypertensive patient was not taken on admission, the doctor may miss out on noting this. Examples of completeness include:

· All relevant forms must be filled completely and signed with the date of attendance

· Nursing notes must include vital recordings, progress notes and nursing plans with signatures and dates of entry

· The patient’s record must include all identifying data such as the patient’s name, age, address, etc.

Formatting And Legibility Makes Data Shareable

Doctors are known for poor handwriting but illegible data is useless to the patient as well as other doctors who may treat the patient in the future. Thus, all data should be correctly formatted and legible. Examples include:

· All codes and symbols used should conform to pre-determined coding standards

· The patient’s demographic and medical data should be readable

· Any abbreviations used should be standardized and understood by all the healthcare professionals involved in taking care of the patient.

Timeliness Adds Value To Primary And Aggregate Data

Let’s say a patient was given an injection but it was not recorded on his file. The doctor on the next shift may not realize this and may repeat the injection. This can have serious ramifications. On a larger scale, if a patient has been discharged but if the records have not yet been processed, the hospital may not get an accurate reading of the current bed occupancy. Thus, data must always be updated and documented as the event occurs. Examples of this include:

· Data identifying the patient should be recorded at the time of first attendance

· Results of physical examination and tests must be updated with every attendance

· On discharge, the patient’s data must be processed, coded and indexed within a stipulated time frame

Accessibility Determines The Usefulness Of Data

Data may be accurate, reliable and up to date but if it isn’t accessible, its value is lost. To meet these criteria:

· Health/medical records must be available whenever needed

· Abstracted data should be accessible for reviews

· Statistical reports should be available to planning and patient-care committees and government organizations

Ensuring High Data Quality

To ensure data quality, information must be validated before it can be entered into the system. For example, let’s say a patient walked into a hospital and gave her name as Teena’ but the name on her hospital records is Tina’. This might lead to the creation of a new patient record. This means the doctor will not know about the patient’s medical history already recorded with the hospital and files will be duplicated. Data may also be lost.

Such common errors could be minimized by validating the patient’s name and address by comparing it to reliable third party databases. It’s not something the admissions office needs to do manually “ there are APIs designed for just this. These software solutions can also be used to run regular checks on the existing database to identify incomplete, inaccurate and duplicate records.

Similarly, when entering vital values and test reports, a validity check must be carried out or the values must fall between certain parameters. If it does not match, the field may be highlighted so that the person entering the values can recheck it. Simple consistency checks can also be put in place to check data accuracy and reliability. For example, male patients cannot be given a pregnancy test.

A Concluding Note

Data quality in healthcare is critical not only for patient care but also for monitoring health care services and developing standards. The ongoing pandemic has been the perfect example of data’s role in healthcare. Data-driven organizations were able to respond swiftly with predictive analytics and patient care models to help the world cope.

Real-time data-driven insights have helped healthcare facilities take critical decisions and keep their doctors and patients safe. The bottom line “ healthcare organizations cannot afford to be casual about data collection and management.