Electronic Health Records (EHR) have been gaining traction. EHR systems and technologies, including processes for EHR implementation and integration, have captured the attention of ISVs.
We have been interacting with CTOs, technology mentors, and heads from various EHR ISVs from North America. We realized that there are many common concerns on the development front.
While we would comment on methodology, communications, code management and engagement level topics in separate blogs, here we enumerate business concerns shared by top executives.
- In most cases, the techie mindset prevails and hence it lacks business and regulatory views. The development teams, either in-house or third-party, do not really go to the depths of understanding of business aspects. They feel happy about the technology depth and build something that never gets user acceptance.
- The healthcare industry-related knowledge transfer overheads while developing specifications that customers expect vendors to know anyways; leading to compounded time, efforts, costs and unhappy users.
- Difficulty in integrating data with existing/new healthcare systems leads to creation of another island app. It is a fact that everyone has something implemented and that investment needs to be capitalized and not written off. The approach must consider integration and migration needs of data in a secure manner as mandated by regulators.
- Adapting to changing dynamics in regulatory needs such as MU1/2/3 etc. is important., Regulator’s guidelines on MU1/2/3 have a bearing on the way EHR products are built. It is important that business aspects are well understood by product and data engineering teams.
- No data engineering for insights and decision-making, less empowered users- EHRs collect a lot of data. Such data is wealth and must be used for crunching to identify insights, patterns etc. and aid decision making and make predictions.
- Lack of knowledge of clinical vocabularies leads to the funny situation of “right hand does not what left hand is doing”- Product teams must know clinical terms, references etc. to an adequate depth to translate the knowledge into a robustly engineered product.
In the next blogs, we would articulate some areas of data engineering and also engagement trends.
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