The collection of quality measures has existed for several years – EMRs have automated some parts of the data collection process, but not everything. Certain measures require data be abstracted from several sources. The data volumes can be staggering; the amount of manual labor can be overwhelming and while both payers and providers strive to optimize operations – there is still significantly more streamlining to be done.
Join payers and providers from around the country as we:
- Review the challenges of the current state
- Consider options for streamlining the end to end processing
- Use technologies to reduce error rates and promote focus on problem areas
- Review technologies such as Natural Language Processing (NLP); Machine Learning; and Artificial Intelligence to reduce the burden of data collection and reporting
- Examine fundamental changes to the work process to reduce labor
- Discuss operational efficiencies that enable more time be spent on addressing quality gaps and not the difficulties of reporting