Do you have one of those desks that has files piled high, sticky notes stuck to almost every horizontal and vertical surface, and you are the only one that understands the “system” to the madness? What if this were the case for your doctor or other health professional: would you feel confident in his or her ability to treat you? Probably not. Yet, many analysts within the healthcare industry are bogged down with hunting and pecking out the needed information to perform their actual job. The requisite for clinical data management in order to have optimal efficiency and effectiveness to make decisions is obvious.
Data management in any general sense may start out small and manageable with no need for databases or external software. Spreadsheets are used to create reports, analyze costs, and keep track of business dealings. These first initial spreadsheets weren’t sufficient after some time and additional reports and information were required, so another spreadsheet or ten were implemented for further insight.
Then, the database or data warehouse option is no longer optional, and the data being entered and tracked is now distributed differently, and access to records is hidden or more difficult to uncover without the aid of the newly-hired IT manager. Subsequently, all the reports that had once been generated and produced helpful vision of the business are now out-of-date due to the difficulty in turning out useful reports.
It is not the mission in a business to mismanage the data, however the fact that the snowball effect can cascade without intervention and appropriate stop-gap measures is very real. Within the business of providing healthcare, the mismanagement of data can happen quickly by virtue of that medical data is one of the highest producers of stored information over most industries. Additionally, the reality that privacy must be protected and accessibility by permissible individuals comes with the territory. Overall, clinical data management is not an easy concept to tackle.
To begin data collection, entry and validation have to be standardized at each workstation connected to the data. Although this sounds pretty much like common sense, it isn’t as easy to implement as you might think. Data collection is usually in the form of the packet of paperwork a patient fills out. This paperwork should contain all the necessary information required for contacting the patient, insurance details, and whatever else is needed internally and externally to that office.
This packet is then turned into someone who will do both data entry and data validation. Data entry is the transfer from paper to electronic form, but this the time to verify that the patient filled in all parts of the paperwork, and if there were any mistakes, these discrepancies are corrected. This can sometimes occur with illegible writing or if the patient doesn’t understand the request being made.
Data validation can start at this point, but can happen with edit check programs or through different departments, such as billing. Each one of these previous steps is vital to producing the most accurate output. Yet, the work at this stage is part of the last measure to meet established protocol and ensure meticulousness.
If discrepancies are found later on, standard procedures should be established and performed to resolve all known errors. If the error cannot be answered with simple investigative tools, the record should be flagged so that further time and energy can be dedicated to achieve the highest degree of accuracy.
This is a simplified look at clinical data management, and there are many other departments and individuals involved throughout the life cycle of a medical file, but with this basic understanding and foreknowledge of some of what takes place within a doctor’s office or hospital, a picture can be formed of just how extensive and expansive one industry has to go to handle so much data.