Opportunity In HealthCare

They key areas of opportunity and growth within in the next year and years to come will be both leveraging currently installed systems and data warehouses to build business intelligence (BI) models. As well as, a growing focus on big data as a pathway to predictive analytics and improving clinical decision support tools and patient care. HIMSS (2014), outlines optimization of current technology as a top priority for the information technology departments. This is consistent with HIMSS (2015) report that indicates over 90 percent of respondents agree that technology is a key factor in at least one of the identified areas of opportunity for organizational success; and HIMSS (2017) findings that put health information technology (HIT) at the high priority and medium priority ranks. With regards to big data and the adoption of electronic health records (EHR), the expansion of data analyzing techniques to be leveraged in predictive models and clinical decision support tools will be a major focus in years to come.

As healthcare organizations begin to feel the effects of newer payment models, organizations are in a position where optimizing current information tools will be key to reduce costs. Year over government has applied regulation to Medicare and Medicaid spending, while not doing enough to regulate private payer costs. As a result the average insurance premium is approaching $20,000 and patients are spending more on services that are of little to no value (Altman & Mechanic, 2018).

By leveraging current information systems or optimizing the way technology is used in an organization. Healthcare organizations will see an impact that will have an upstream effect. Considering current conditions of health IT many organizations are leveraging different team based care models. A major factor in team-based care models is the IT infrastructure. The IT infrastructure will be the major expense that organizations have depending on how well they can leverage current technology to reduce investments costs (Reiss et al, 2016). Moreover, in the short term building efficient BI models by leveraging disparate data sources will be impactful on organizations that rely on data to make decisions. The impact of an effective BI model in healthcare can improve various areas of an organization. BI models are not intended for siloed use rather are intended to bring data to end users in different area of a healthcare organization. Building an effective BI solution can be short term or part of a long-term initiative to create sophisticated analysis for predictive models. In the short term the goal could be to focus on leveraging BI for workflow analysis and creating efficiencies to workflows across many departments of an organization (Madsen, 2012). By improving workflow efficiencies this would not only impact stakeholders but patients alike. The impact pertains directly to faster responses to patient needs and care.

With regards to long-term initiatives the focus on building predictive models by leveraging the big data sources will have a lasting impact on various areas of the healthcare industry. More notably are the potential impacts on costs, and improvement in high-risk patient care. Big data is nothing new to healthcare. The only difference is now we have manageable repositories that we can leverage to query data. With predictive models some areas to focus are on the higher risk patients. These are patients with chronic conditions or patients who consistently visit the emergency room or urgent care centers. By leveraging data from the EHR, organizations can better place patients in subgroups from high risk to low risk and better determine who will be higher cost patients. This sort of analysis will allow an organization to place predictive measures to better respond to the needs of certain patients in the higher risk groups with the end goal of reducing costs per admission and improving the management of their care (Bates et al, 2014).

Although there are multiple facets of the healthcare industry that informatics will impact long term. My assumption for years to come is that BI and different analytical models that can be leveraged to review and create predictive models out of the big data, will be a tremendous area of opportunity within the next year to next five years. As healthcare begins to fully integrate IT into the various industries that include pharmacy, durable medical equipment, ambulatory centers, and other ancillary providers or healthcare facilities. We will begin to see organizations learn to leverage their data to manage costs, manage workflow, and improve patient care.

References

Atman, S., & Mechanic, R. (2018). Health care cost control: Where do we go from here. Health Affairs. Retrieved from: https://www.healthaffairs.org/do/10.1377/hblog20180705.24704/full/

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.

HIMSS. (2014). 25th Annual HIMSS Leadership Survey. Retrieved from: https://uic.blackboard.com/bbcswebdav/pid-6522713-dt-content-rid-67672640_2/courses/2019.spring.bhis.530.17737/2018.fall.bhis.530.30540_ImportedContent_20181015062912/2014-HIMSS-Leadership-Survey.pdf

HIMSS. (2015). 26th Annual HIMSS Leadership Survey. Retrieved from: https://uic.blackboard.com/bbcswebdav/pid-6522713-dt-content-rid-67672641_2/courses/2019.spring.bhis.530.17737/2018.fall.bhis.530.30540_ImportedContent_20181015062912/2015%20HIMSS%20Leadership%20Survey.pdf

HIMSS. (2017). 2017 HIMSS Leadership and Workforce Survey. Retrieved from: https://www.himss.org/sites/himssorg/files/FileDownloads/2017%20LEADERSHIP%20and%20WORKFORCE%20SURVEY_Summary_Findings_Final.pdf

Madsen, L. (2012). Healthcare Business Intelligence: A Guide to Empowering Succesful Data Reporting and Analytics. Hoboken, NJ: John Wiley & Sons Inc.

Reiss-Brennan, B., Brunisholz, K. D., Dredge, C., Briot, P., Grazier, K., Wilcox, A., ... & James, B. (2016). Association of integrated team-based care with health care quality, utilization, and cost. Jama, 316(8), 826-834.