Thursday, July 23, 2015
A Primer on Population Health
If you need to help a colleague who is clueless about cost-saving potential of population health, the Population Health Blog recommends you refer him or her to this efficiently written primer from The Health Care Transformation Task Force's High Cost Patient Work Group.
Basically, if the small number of patients who are destined to be high cost can be prospectively a) identified and b) helped, a lot of money could be saved. By focusing resources on a small fraction of insured people, the costs for the entire risk pool can be decreased.
In the meantime the majority-remainder of the population can be aided with other lower-cost resources that include, but are not limited to wellness and prevention.
Who are the small numbers of people?
1) patients with a) advanced illness who are nearing end of life who may benefit from b) hospice.
2) patients with a) high spending patterns who may benefit from b) coordination of care by a dedicated health provider (such as a nurse or a lay community care worker).
This population does not include:
3) high cost patients with a) any illness who b) are destined to get better all by themselves or c) unlikely to derive any benefit.
Algorithms to spot that small number of patients use diagnosis codes, treatment codes and medication utilization data from the electronic record or insurance claims databases. Other useful insights can be gained from patient surveys (and a number are available), using public data to ascertain socioeconomic status (zip codes are destiny), asking physicians to refer patients who are at risk and the design of insurance benefit (patients may not be aware that certain services are covered).
The science behind the use of these inputs is imperfect but getting better, and the more inputs, the better. Don't let the perfect be the enemy of the good, however, because simple algorithms based on readily available data will get you started.
One of the advantages of this approach is that the cost of coordination of care is variable. It can start small and be flexed up as expertise grows and opportunities arise
The Population Health Blog couldn't have said it better.
Basically, if the small number of patients who are destined to be high cost can be prospectively a) identified and b) helped, a lot of money could be saved. By focusing resources on a small fraction of insured people, the costs for the entire risk pool can be decreased.
In the meantime the majority-remainder of the population can be aided with other lower-cost resources that include, but are not limited to wellness and prevention.
Who are the small numbers of people?
1) patients with a) advanced illness who are nearing end of life who may benefit from b) hospice.
2) patients with a) high spending patterns who may benefit from b) coordination of care by a dedicated health provider (such as a nurse or a lay community care worker).
This population does not include:
3) high cost patients with a) any illness who b) are destined to get better all by themselves or c) unlikely to derive any benefit.
Algorithms to spot that small number of patients use diagnosis codes, treatment codes and medication utilization data from the electronic record or insurance claims databases. Other useful insights can be gained from patient surveys (and a number are available), using public data to ascertain socioeconomic status (zip codes are destiny), asking physicians to refer patients who are at risk and the design of insurance benefit (patients may not be aware that certain services are covered).
The science behind the use of these inputs is imperfect but getting better, and the more inputs, the better. Don't let the perfect be the enemy of the good, however, because simple algorithms based on readily available data will get you started.
One of the advantages of this approach is that the cost of coordination of care is variable. It can start small and be flexed up as expertise grows and opportunities arise
The Population Health Blog couldn't have said it better.
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment