Case Study - Vermont Information Technology Leaders (VITL)

Summary

Vermont Information Technology Leaders (VITL) describe their process of connecting 14 hospitals to Vermont’s health information exchange (VHIE). Essentially their goal is to provide access that would allow healthcare providers to leverage shared data across a network of health systems, with an end goal of improved reporting, reduced cost to healthcare providers, and improved patient care. They utilized a central data warehouse of which they further siloed the data into separate data marts for access. They also utilized continuity of care documents (CCD) and practice profiles as a baseline for creating the blue print for how they would define required data elements for ACO’s. Particular paths taken to improve reporting where physician training on data entry and establishing reporting metrics based on parsed CCD’s (Raths, 2016).

Problems Statement

Although VITL has been successful in implementing an HIE that is critical for the success of specific ACO’s in Vermont. Their solution could not be widespread as a universal practice across multiple states who are looking to implement an HIE. Although some practices they used to implement their program are intuitive and follow best practice as it pertains to interoperability. The overall implementation of the project does not involve their clients input which could halter quality of data metrics. Hoffer (2016) identifies key deficiencies including lack of qualitative measures to identify VITL’s impact on performance and missing functional and performance requirements of the data warehouse. In essence the overarching dilemma with their technology is the lack of input from the impacted organization/ACO’s, and governance oversight. However, with regards to interoperability their model of leveraging CCD’s, and established interfaces is a model that can be mirrored.

Analysis

Standardized Data

As explained the model for data collection and standardization is based on CCD’s, claims, and clinical data information. This information follows current best practice as it pertains to leveraging high level patient information in a broader context and easier to implement (Ferranti et al, 206). The issue being lack of organizational or governance involvement. Moreover, the argument towards leveraging organizational resources cannot be overstated. According to Madsen (2012), a business intelligence solution should include feedback from multiple business units within an organization. These members are responsible for identifying the appropriate use of data and aiding in building trust in the data. What leads to trusted data are the users who can assist identifying trends that essentially effect reports. The business units who are involved in a BI project would essentially deliver better feedback on missing data elements or inefficiencies that effect data. Thus, leading to more effective reporting tools and improved processes (Madsen, 2012).

Data Warehouse/Data Mart

With interoperability being at the forefront of scrutiny in today’s healthcare environment. VITL did provide a baseline for leveraging established interfaces to collect data. Moreover, utilizing CCD’s which is a joint effort of Health Level 7 (HL7) and the American Society for Testing and Materials (ASTM) to support a more standardized method of sharing data. Is an important and critical component of VITL’s better practices. This model could be leveraged across the board for organizations small or large who are looking to leverage a health information exchange (HIE). The significance of CCD’s is the semantic interoperability this methodology entails. The potential to leverage established interfaces and utilize pre-established data points which forges the need to share data between disparate systems (Ferranti et al, 2006). The significance of interoperability in today’s healthcare environment between multiple interfaces is significantly important. As organizations have taken on “best of breed” technology, the need for communication between disparate systems is more important than ever before. Even as we move towards single vendor solutions the guarantee of interoperability is still an issue. Most organization who claim to offer integrated services still offer bundled technologies under one roof (Spooner, Reese, & Konschak, 2012). By implementing data marts and leveraging the tools that are aforementioned. VITL’s solution is on the right path to successfully integrate an adequate BI solution to improve interoperability, data quality, cost, and patient care.

Recommendation

With regards to the aforementioned issues with performance and qualitative metrics that are missing. I would leverage a data governance team composed of stakeholders from different business units within the organization. These users can be subject matter experts from different units, business representative, and clinical staff. The end goal should be to leverage these individuals to ensure that we are reporting on metrics that are not only quantitative, but also qualitative. The idea is to ensure we are producing high quality trusted data that can allow for actions to be taken on business decision and patient care (Madsen, 2012). Moreover, being able to determine and identify key areas for improvement would allow the organizations to implement processes that would improve the overall data quality. The focus on data quality could be leveraged to improve on data that is being reported to an HIE and shared to organization that will result in better continuity of care.

Conclusion

There are both aspects that can be modeled and can be improved on. Particularly as it pertains to leveraging current interfaces to pull data into a data warehouse. Also, by leverage CCD’s which are semantically interoperable solutions that are recommended by HL7 and ASTM. Being able to leverage this model will aid in connecting disparate systems. However, as it pertains to best practices in data quality. Leveraging business units would be a better solution to understand functionalities and improve reporting. VITL’s solution like other implementation process can be leveraged as a business model in interoperability. However, in reporting and data quality more evaluation is needed to improve current processes.

Reference:

Ferranti, J. M., Musser, R. C., Kawamoto, K., & Hammond, W. E. (2006). The clinical document architecture and the continuity of care record: a critical analysis. Journal of the American Medical Informatics Association, 13(3), 245-252.

Hoffer, D. (2016). Vermont Information Technology Leaders, Inc. (VITL) The State Has Begun to Address Oversight Deficiencies, but Has Limited Measures in Place to Evaluate Performance. Report of the Vermont State Author. Retrieved from: https://auditor.vermont.gov/sites/auditor/files/documents/VITL%20Final%20Report%20-1.pdf

Madsen, L. (2012). Healthcare business intelligence: A guide to empowering successful data reporting and analytics. Hoboken, N.J: John Wiley & Sons.

Rath, D. (2016). Vermont HIE Focuses on Data Quality to Power ACOs. Retrieved From: https://www.healthcare-informatics.com/blogs/david-raths/vermont-hie-focuses-data-quality-power-acos

Spooner, B., Reese, B., & Konschak, C. (2012).  Accountable care: Bridging the health information technology divide (1st ed.).  Virginia Beach, VA: Convurgent Publishing.

Vermont Information Technology Leaders. (2015). Transforming healthcare through technology annual report 2015. Retrieved from: https://www.vitl.net/sites/default/files/documents/Annual-Reports/2017-vitl-annual-report-final.pdf CasestudyVHIE.BHIS529.docx