The results of spending billions of dollars on EHR implementation through federal projects like the meaningful program have been mixed, to say the least. Some practices report positive results and praise their EHR systems for improving patient outcomes or reducing workgroups. Others complain about the need for increased documentation or blame EHRs for the deteriorating doctor-patient relationship. While some clinics still struggle to effectively use EHR systems, others are already looking forward to the next phase of EHR use.
Until a few years ago, the main focus was on getting started with EHRs and collecting of data. Most practices had years worth of paper files that needed to be digitized before they could be of any use with new systems. Many healthcare providers did not realize the magnitude of the project until they had already started deployment. While entering new patient data into EHR systems was relatively easy, converting old records often took a while.
The Shift from Data Collection to Data Analytics
However even those who are disappointed by the results – or lack thereof – from EHRs, are optimistic about data analytics. Most organizations are early doctors when it comes to data analytics and very few have achieved meaningful results so far. However that does not deter the optimism surrounding this type of technology. Many people within the industry refer to data analytics as the EHR ‘Golden Egg.’
So how does data analytics differ from data collection? When practices switch to EHRs from paper-based filing systems, the biggest priority is to bring data into the software. That data has to be cleaned and curated as well. Quite often mistakes made in the paper files carry over into the EHR data, resulting in inaccurate databases. Making decisions with erroneous data will naturally not provide optimal results.
Once an organization as accurate data, it is time to move on to the next stage – analyzing it. Digitizing healthcare data unlocks the opportunity for further analysis and research. It makes it easier for doctors to identify trends, group patients by demographics or other variables and spot markers for illnesses that are not readily apparent. This type of analysis can help in identifying diseases earlier. For illnesses like cancer or heart ailments, early detection can make a significant difference in morbidity and mortality rates.
Obstacles Abound for Data Analytics
Unfortunately most healthcare providers are still in the initial stages of the EHR implementation when it comes to data. Practices still struggle to clean up and remove inconsistent data from the EHR software. Many factors contribute to such mistakes in the underlying database. Anything from inadequate training to poor EHR design can exacerbate the problem. There is a pressing need for healthcare professionals to become proficient with EHR systems before moving onto the data analytics stage. Quite often, end users are content to simply enter data into the system. Very few have the time to understand more complex requirements and operations.
Another obstacle in the path of data analytics is a limitation of the software itself. Some vendors are still working on being the basic design of their EHR systems. Not many EHR applications have the advanced capabilities to perform data analytics even if the organization is ready for it. If a tool is extremely complex to use, how many people will want to spend time with it?
In spite of these obstacles, the healthcare industry continues to be optimistic about the potential of data analytics. Those who have positive experiences with EHR systems look forward to further improvements. Even practices which have not seen the promised benefits of EHRs are enthusiastic about the potential here. It remains to be seen if or when those promises will be fulfilled.