How to Decide on a Healthcare Data Solution Model

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Data in healthcare is crucial to many different people for many different reasons and requires a lot of attention paid to how it is treated. Coming up with a healthcare data solution model is no small feat, and has long lasting effects on both the patients whose information is stored there, and the professionals that provide services all along the way. But data is a fickle thing, or better stated all the ones and zeros that make the data digital are the things that are the difficult to work with.

In many other industries, a well-laid plan must be present in order to provide a good or service, such as an architect doesn’t just create a somewhat specific drawing of a building to be constructed, but goes about researching and implementing vast amounts of knowledge and details and then provides a very precise blueprint as to what the building will be, what it contains, where plumbing and electricity will run and adheres to structural soundness. In the healthcare industry, the plan is not so easy to design due to ever-changing regulations from government and insurance companies, bigger demands or goals within the organization, and modifications that are made as discoveries are found within.

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Constructing a database, data warehouse or data management system requires some concrete details, but also has to have the ability to change and grow as necessities call for them. This is a huge task being asked of software, not insurmountable, but one with that requires some leniency to rules. Luckily, in software-speak there is such a thing as a late-binding data approach that allows for the specific information that is being called up to be requested at the last possible moment, which means that the information is not tied to specific rules.

There are some pros and cons to this type of handling of data, as with most everything in life. On the negative side of this type of function you are looking at a less stable platform on which the data sits; because data and data categories aren’t subject to guidelines that tell them what to do, what they represent and when to come forward. This makes the data volatile and if it isn’t treated just right, data results will be inaccurate. However, the volatility is becoming more of a non-issue as software developers understand the dynamics and are able to program the software to respond without errors.

On the other hand, the positives of having a healthcare data solution model that uses late-binding include the ability to add, change and modify the database or data warehouse as needs change. When creating a data solution model most, if not all of the structure for the system must be laid out, which can be difficult in healthcare because the needs are always changing, goals are refined and regulation requirements are revised. Within a rigid system that works with early-binding techniques, the shelf-life, or life of that software, is greatly reduced, while the shelf-life of a late-binding system is better able to adapt and thus usable for much longer.

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Having the ability to fine-tune goals and needs as time goes on is what makes healthcare data solution models so important, especially as progress is made to involve more technology into daily healthcare activities. We have already seen the move from almost all hardcopy medical records to the mandate that they all be digital and available for other providers to utilize. This allows for more accurate and efficient care, without the inefficiencies of copying and physically transferring a patient’s health record, duplication of treatments or procedures, and insufficient information if a patient isn’t seeing their normal physician.

Telemedicine still has a long way to go before being fully accepted by both patient and professional, but there are great strides by some organizations to allow for video conferencing with a doctor, instant messaging, or patient portal communications, which works much like email, and all of which free up time for doctors and office staff because less time and effort is needed for these types of “visits” as compared to in-office visits.

The patient portals work for much more than just communication between doctor and patient: it allows the patient to see results from tests, check on and reorder prescriptions, and send reminders for upcoming appointments and possible need to make appointments. Easy access to information like this helps to put more responsibility on to the shoulders of the patient, as wells as taking some of the pressure of scheduling off the shoulders of the physician.

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Probably the most important aspect of all of these technologies and information comes down to data. Data is where discoveries are made, patterns are determined and predictive qualities are established. The storing, accessing and protection of data is essential for everyone involved.

  • Patients retrieving their own records and information
  • Physicians adding to and looking up patient documentation
  • Financial officers billing for services provided
  • Organizations establishing more efficient programs and eliminating waste
  • Government administrations receiving appropriate reports from organizations

Healthcare data solution models have so many far-reaching consequences and people who depend heavily upon them. Making a decision as to what works best for each individual organization is a daunting task, and one that should be researched and handled very carefully. Knowing the needs of the institutions involved and what future needs or goals may be required will help to facilitate the decision-making process into that data solution model.