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How population health tools can better manage infectious disease

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In real life, disease outbreaks never occur as they do in the movies. Patients don’t steadily stream into the same emergency department, presenting symptoms of a mysterious affliction while clinicians race around with federal agents to pinpoint Patient Zero. The Hollywood version makes for a compelling story, but it doesn’t reflect the reality of modern infectious diseases.

From my experience, most infectious diseases are known threats, but it can take a long time for an outbreak or health emergency to be officially declared, as we learned most recently from the Zika virus and as we seem to learn every year with influenza.

Until then, each patient presents as a single case and, because of our fragmented healthcare system, it is often difficult to link cases, share information and collaborate on a response. However, with population health management tools, it’s possible to connect disparate systems and data.

While they may lack in dramatic flair, these solutions can help change the way we fight infectious disease. Population health management tools offer some of the best new opportunities to advance our understanding of how disease outbreaks occur and, most importantly, how to prevent and respond to them before they get out of control.

At a time when diseases once thought to be isolated to other regions of the world have surfaced in the United States—ranging from drug-resistant infections to African trypanosomiasis, or sleeping sickness—we need better systems in place to exchange information with global health professionals and to work together to contain infectious illnesses wherever they occur.

We’re still a long way off from a time when big data can be used to predict, with certainty, where the next disease outbreak will occur and how best to address it. But there are actionable steps we can take to get there. Beginning to integrate public health data into population health solutions, building information technology (IT) systems that enable greater collaboration and analysis of factors that influence patient health, and implementing protocols for patient engagement will take us three steps closer to the future of infectious disease management.

Integrating public health data. Trusted sources like the Centers for Disease Control and Prevention (CDC) have made vast libraries of health information available to the general public. This data is currently siloed from electronic health record (EHR) programs and physicians’ workflows, however, and it remains difficult to deliver pertinent information about infectious diseases when it’s most needed – at the point of care, or even better, ahead of time.

Integrating CDC or Food and Drug Administration (FDA) data, information from state or local health departments or hospitals (such as antibiograms or lab results trend reports), as well as non-governmental sources such as web search vendors, can help build an early warning system. Because timely and appropriate antiviral therapy can be effective in improving infectious disease outcomes, such a system could have a direct impact on patient health, experience of care and even cost of care.

Some of this work is starting to happen now as the Centers for Medicare and Medicaid Services (CMS) make datasets available, for example, through iBlueButton or Medicare Limited Data Set (LDS) Files. Other partnerships also could support public-private data integrations. For example, universities could partner with health insurance companies and other private entities—keeping privacy, confidentiality and data security in the forefront—to examine trends and explore risk models.

Ensuring that health IT architecture supports infectious disease prevention and management. Accessing public health data and other information not collected in a clinical setting is just the beginning. Putting this data to work to perform effective risk modeling requires interconnecting all of these external inputs with a healthcare system’s data and health IT footprint.

The ability of health systems to accurately predict possible public health issues increases as more dataset systems are integrated. Last year, Kaiser Permanente analyzed its EHR data to create a heat map of Bay Area communities at increased risk for contracting measles, hepatitis A and B, and other infectious diseases. It matched EHRs to home addresses of members who opted not to vaccinate their children, revealing precisely where medical staff could target vaccination efforts. This type of analysis can be built into a population health management system for tracking, monitoring, and engaging patients.

Implementing an engagement strategy. After integrating new data inputs and interoperable health IT solutions for infectious disease prevention and management, the final piece of getting ahead of an outbreak is implementing a patient engagement strategy. This might entail sending emails and texts or even making phone calls to relate relevant information.

Care teams can consider performing a patient survey on an annual basis and asking for a preferred method of communication, as well as the contact details of a caretaker, and recording this in EHRs to streamline engagement efforts.

With a patient engagement strategy in place, a future state version of managing an outbreak with population health management tools might look like this, taking the flu as an example:

  1. Working with the CDC, we might learn that there’s a particularly virulent version of the flu coming this year. Analytics reveals specific communities with large senior populations that have lower flu vaccination rates and poor access to public transportation. In addition, regional data shows that search rates for flu symptoms are going up.
  2. This information spurs a targeted public health initiative to prevent a flu outbreak among seniors, who are particularly at risk for developing health complications. Health system staff contact elderly patients to encourage them to visit a local clinic or pharmacy to receive their flu shots, and clinicians hold flu shot drives at senior centers.
  3. All regional doctors are encouraged to proactively speak to their patients about vaccinations and ask if they are currently experiencing any flu-like symptoms. Patients are engaged during their scheduled appointments and through text or email to ensure that this pertinent public health information reaches more people in the identified communities.

This scenario fits nicely into doctors’ workflows and patients’ lives. By taking what people normally do when they start to feel sick—searching online—and pairing this information with CDC and EHR data, we can set public health initiatives in motion and enable a connected health experience that staves off or shortens an illness.

This is vastly preferable to the situation today, where people think they might be getting sick, wait a day and find out they actually are sick, and then, once they finally see a doctor, their window to get well soon may have already passed. This proactive care can apply to other illnesses as well, including non-infectious diseases.

Global travel and commerce, changes in how and where our food is raised and delivered, and other environmental changes are expanding the reach and prevalence of various infectious diseases. By integrating public health data, investing in a health IT architecture that supports infectious disease prevention and management, and strategically engaging patients to deliver more timely care, clinicians around the globe can perhaps contain or even prevent the next outbreak. Because when it comes to patient health, we can leave the dramatic interventions to Hollywood.

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