An excellent population health management approach considers multiple aspects of health, such as sufficiency of medical care, public health interventions, social and physical environments and related services, genetics, and individual behavior. (1) Data from multiple sources can be used to conduct population health management programs and to assess their value. In addition to these diverse data, data from big population health programs are driven by huge volume, high speed, and incoherent data flows. (2)

Population health programs improve health by catering to individuals with unmet medical needs as well as taking actions to close these gaps; thereby encouraging quality in health care that focuses on getting the right care to the right patient at the right time. (3,4) Given the extensive potential of population health management, its execution may require maximum utilisation of big data sources existing across the healthcare and other social systems. (5)

Advanced data analytics, when used appropriately, has the potential to improve patient care in the health care system. With the shift in healthcare towards outcome and value-based initiative, analysis of existing data to determine the most efficient practices can enable cost reduction and improvement in population health. With more and more healthcare systems becoming data-reliant, insights regarding health of population along with data on high-risk individuals can be understood with the help of data analytics. With this knowledge, the health systems can more competently assign resources thus maximising revenue, population health and, most importantly, patient care. (6)

The ongoing big data utilisation by population health programs is capable to get even bigger in time to come. Multiple data types are usually used to manage such programs. Clinical data includes information on engaging health management programs as well as data on health risks collected via survey-based health assessments. External data sources describing characteristics of home life, neighbourhood, and the local supply and quality of health care can provide understanding of the origin of social or physical environment. Yet, other survey data can be helpful in assessing health-related quality of life (HRQoL) and insurance arrangements, perceptions of access to care, and the quality of care received. (2)

Delivering practical insights to help providers get an understanding of chronic diseases as well as tracking the success of practice transformation processes are two main driving forces while developing big data analytics infrastructure and reporting during population health programs. It is imperative to optimize electronic health records (EHRs) to collect some data elements in a more standardized way, and to emphasize on executing data governance programs that account for currently unstructured data as well as data that to be structured better. (7)

Implementing a robust framework of good governance will help providers and payers to move towards desired clinical and financial improvements in the near future. The management of population health programs has proven beneficial with recent advancements in big data; especially, when big data is successfully leveraged to execute a comprehensive program. Additionally, data generated from these programs have been used to routinely monitor program performance and to conduct in-depth program evaluations. Further advancements in technology will certainly improve population health and social service programs through the use of big data. (2,7)

References

  1. Kindig D, et al. What is population health? Am J Public Health. 2003; 93:383.
  2. Wells ST, et al. Leveraging big data in population health management. Big Data Analytics. 2016; 1:1.
  3. Chen EH, et al. Improving population health through team-based panel management: comment on “Electronic medical record reminders and panel management to improve primary care of elderly patients”. Arch Intern Med. 2011; 171:1558–9
  4. Clancy CM. What is Health Care Quality and Who Decides. Committee on Finance Subcommittee on Health Care United States Senate, Agency for Health Care Research and Quality, U.S. Department of Health and Human Services. 2009.
  5. Hartman C. Healthcare’s growing data opportunity. Leveraging clinical intelligence to elevate population health management strategies. Health Manag Technol. 2014; 35:24.
  6. How healthcare analytics improves patient care. 
  7. Bresnick J. Population Health is Top Data Analytics Challenge for Providers, Payers. HealthIT Analytics. 

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