Throughout the course of this pandemic, a great deal of attention has been paid to testing. Most of us think of testing as a single person driving through a tented testing site and waiting a few days for their swabbed results to be returned to them. Pooled testing is no different from the tested person’s standpoint: you still have a swab test just as if you were being individually tested, but the difference is in how the lab processes the collected tests and how the results are reported.
In pooled testing, biological samples from several people are mixed together prior to analysis. If the pool is negative, a large group of people can be deemed to be negative. The advantages to doing so are several:
Performing a single pooled test can quickly and efficiently check a large sample of people for presence of COVID.
The cost of a pooled test that includes many people is substantially less than the costs of individual tests for the same number of people.
Capacity of testing increases tremendously, permitting high numbers of people to be tested frequently with much less strain on the laboratory systems.
There are several caveats to be aware of, however, that make pooled testing slightly less than perfect. When pooled testing yields a positive test, it is impossible to know which pooled specimen is positive, so they must be retested individually. This is fine if a positive test appears only infrequently of if the pool is small. Pooled testing is best used in populations where positive tests are not expected: think colleges rather than nursing homes; communities who have not seen an outbreak versus an area with a lot of COVID hospitalizations.
Strategies such as split testing can help with the work of retesting positive results. If a pooled test shows a positive, the pool samples are split in two groups and retested. If one of the splits is positive, that sample is split again and retested while the negative split is cleared. Rinse and repeat as necessary. This reduces the amount of retesting necessary and eliminates fairly large groups of negatives.
There is also the issue of false negatives, which are slightly higher in pooled tests than in individual tests due to the diluted samples in a pool. Analyzing the pooled sample more than once appears to reduce this risk, but even single tests have false negatives.
Pooled testing is an effective, efficient strategy in communities where COVID-19 is not prevalent and early detection is desired. It safes time, money, and leads to earlier control through contact tracing and quarantine. Every day people with COVID are undetected and mix with their immediate population, the risk of uncontrollable spread rises exponentially. Several of the countries who have had the most success in managing their pandemic outbreaks have relied on pooled testing and research from Israel has shown that pools of 32 samples can detect a single positive sample (with a 10% false negative rate). Greater samples, up to 64, can be detected as well but require a technique called amplification samples. The US should be leveraging the pooled methodology immediately in areas where it is applicable to improve our capacity for testing and increase the speed of detection in the community. And once there are detected positives, using a remote patient monitoring solution to track progress of those who are positive will further help extend thinning healthcare resources.
Population health addresses the health status and health issues of groups of people or populations. It brings significant health concerns into focus and addresses ways in which communities, healthcare providers, and public health organizations can allocate resources to overcome the problems that drive poor health conditions in the population, e.g. diabetes, obesity, autism, heart disease, etc.
Information technology is a part of the core infrastructure on which population health can be assessed and addressed. Successful population health programs utilize IT and significant change in the way providers deliver care – based on collaboration, communication, information exchange, analytics, and approaches to identify and manage gaps in care for high-risk patients.
This Digital Rochester event will provide attendees with insights into the activities and initiatives that local health systems, provider groups, and support agencies, and local and national population health solutions providers and are doing to analyze population health information to improve health outcomes in our community and nationally.
Speakers include: Dr. Tom Mahoney, Chief Medical Officer, Finger Lakes Health Systems Agency LaRon Rowe, Director, Information Management, Accountable Health Partners Jaime Hayslip, Chief Information Officer, GRIPA
Jose Rosario, Sr. Director of IT and Analytics, FLPPS
Dr. Martin Lustick, Sr. Vice President and Corporate Medical Director, Excellus BCBS
Event Moderator: Al Kinel, President, Strategic Interests LLC
Most hospitals and health systems in the US have instituted EHRs across their enterprises for health data management. However, the realization of value from that significant investment is sometimes lacking. This is often due to improper or incomplete installation, failure to leverage features or an organization that has not otherwise prioritized initiatives to extract value from their EHRs.
CIOs in healthcare are now seeking and forming programs to increase the benefits their hospitals and health systems can gain from electronic health record systems. According to a recent survey of the 1,400+ members of the College of Healthcare Information Management Executives (CHIME), the HIT industry should expect an increased focus on the optimization of EHRs. More than 70% of responding CHIME members stated that this coming year’s top IT priorities for their organization will be projects that harness value from their EHRs. And of these respondents, almost three quarters plan to utilize outside firms to assist their internal teams with these projects.
This is a forward-thinking and positive trend which should have tangible benefits on an organization’s bottom line as well as yielding outcomes-based improvement for stated initiatives. More data is of no benefit without using that information in a positive way. Programs like the Unity Health System’s Community Diabetes Collaborative (CDC), which helped Rochester-area patients with diabetes improve their blood glucose levels by 14 percent in the first 18 months, take what could have been background noise and transform it into population health initiatives that improve care quality and save money.
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