400418 Health Advancement: Strengthening Public Assessment Answer

Answer:

Reviewed Article: Strengthening Public Health and Primary Care Collaboration Through Electronic Health Records

Authors: Neil Calman, MD; Diane Hauser, MPA; Joseph Lurio, MD; Winfred Y.Wu, MD, MPH and Michelle Pichardo, MPH.

Electronic Health Records (EHRs) can help in improving the coordination among primary care providers and public health departments for the insurance and maintenance of well being in the population. This system allows documentation of the health data of the patient, and can improve surveillance of chronic or communicable diseases, facilitate critical alerts in relation to public health goals and objectives that works real time with the diagnosis and treatment (Varonen, Kortteisto, & Kaila, 2008)

How can Electronic Health Records improve support and management of surveillance and management o

f chronic and communicable diseases:

The authors of the selected journal points out that EHR can advance monitoring and supervision of both chronic as well as communicable diseases.

Syndrome Surveillance that allows monitoring of symptoms that may represent a communicable or infectious disease (Overview of Syndromic Surveillance What is Syndromic Surveillance?, 2017). This can be aided with EHR by automatic reporting of data to the concerned department of health (Birkhead, Klompas & Shah, 2015). The system can transmit routinely collected signs and symptoms reported by the patient along with demographic information, which can be used to make a map of prevalent symptoms in association with different locations. This can help to find any patterns of outbreak.

The system can create alerts that can act as reminders for the providers during diagnosis of reportable disease, also providing a hyperlink to the required reporting form (Schiff et al., 2017). The system auto populates the demographic information, reducing the workload of the provider. The system also maintains lead screening and immunization data, which is automatically uploaded to the server’s thereby eliminating manual uploads. This data can then be used by providers, to check immunization histories for patients, preventing gaps or duplication in immunization schedule (Shankar et al., 2016). Findings from epidemiological investigations can be distributed through Health Alert Networks (which is participated by nearly all public health entities).  In case of local increase of diseases, an alert is sent to the providers in that location only. In any case, these alerts can induce the healthcare providers to initiate swift action in treating the individuals suspected of these emergent medical conditions. EHR can also ensure active participation of the healthcare providers to be involved in an active role in the surveillance of diseases. Providers can help in the collection of specimens or samples from patients that can be used in research and surveillance (Gale et al., 2017).

EHR can also assist in the surveillance and management of Chronic Diseases, by reflecting public health agendas that prioritizes care for diseases that has significant impact on the public health (Perlman et al., 2017). The system allows incorporation of the alerts based on the guidelines, and facilitate adherence, using disease registries and and reports. This can then prompt the providers to identify patients overdue for tests, screening or visit. The alerts also help the providers to provide appropriate referrals to patients, for further checkup and treatment. The EHR data can help to identify the risk groups for certain chronic diseases or conditions, which can improve early detection and motivate preventative bhaviour. The data, along with the demographic information can be used for screening of specific diseases among immigrants from countries where the incidence of the disease is high. Exchange of this information between public health, primary healthcare centers, individuals and the population, can therefore allow better surveillance and management of these diseases.

Health promotion and disease prevention practices in New York:

The HER system was developed, tested and implemented by New York City Department of Health and Mental Hygiene (NYC DOHMH) and The Institute of Family Health, and is used to monitor notifiable conditions like tuberculosis. NYC DOHMH has implemented public health alerts for diseases like West Nile Virus through Health Alert Network, prompting the healthcare providers to contemplate this disease for patients complaining of headache and fever (Asnis et al, 2000; Calman et al., 2012).

During the H1N1 breakout, data about the percentage of patients reporting influenza like symptoms helped in the identification of peak months of infection, and redistribute immunization resources accordingly, as shown in the figures below:

Figure 1: Percentage of patient visits at New York City sites for influenza-like illness (ILI): Institute for Family Health, June 8–December 4, 2009.; source: (Calman et al., 2012) 

Figure 2: Percentage of patient visits at New York State’s Mid-Hudson Valley sites for influenza-like illness (ILI): Institute for Family Health, June 8–December 4, 2009.; source: (Calman et al., 2012)

The NYC DOHMH also monitors the incidence of diseases and medical conditions (like tuberculosis) using registers and reporting of the notifiable diseases (as in case of West Nile Virus).

In cases of chronic diseases, NYC DOHMH implemented the Take Care New York (TCNY) initiative in 2004. It identified 10 goals for the New York population for better health. The goals include: having a frequent doctor checkup, being tobacco free, keeping a healthy heart, knowing the HIV status, getting help for depression, living free of drugs or alcohol, checking for cancer, getting immunized, maintaining safety and well being at home, and delivering a baby in good health (Calman et al., 2012). EHR alerts are made keeping in mind these goals, and hence can raise a notice if such goals are jeopardized. This can in addition help to educate the patients. A record of these alerts can provide a pattern of incidence, which can help in better management of the disease.

References:

Asnis, D. S., Conetta, R., Teixeira, A. A., Waldman, G., & Sampson, B. A. (2000). The West Nile virus outbreak of 1999 in New York: the Flushing hospital experience. Clinical Infectious Diseases, 30(3), 413-418.

Birkhead, G. S., Klompas, M., & Shah, N. R. (2015). Uses of electronic health records for public health surveillance to advance public health. Annual review of public health, 36, 345-359.

Calman, N., Hauser, D., Lurio, J., Wu, W., & Pichardo, M. (2012). Strengthening Public Health and Primary Care Collaboration Through Electronic Health Records. American Journal Of Public Health, 102(11), e13-e18. https://dx.doi.org/10.2105/ajph.2012.301000 

Gale, J. Y., Lewis, V. R., Fantegrossi, A., Sinnette, C., Griswold, K., Gorman, J., & Schuur, J. (2017). 360 Specimen Collection and Labeling After Implementation of New Electronic Health Record: Work as Imagined Versus Work as Performed. Annals of Emergency Medicine, 70(4), S142.

 Overview of Syndromic Surveillance What is Syndromic Surveillance?. (2017). Cdc.gov. Retrieved 28 November 2017, from https://www.cdc.gov/mmwr/preview/mmwrhtml/su5301a3.htm

Perlman, S. E., McVeigh, K. H., Thorpe, L. E., Jacobson, L., Greene, C. M., & Gwynn, R. C. (2017). Innovations in Population Health Surveillance: Using Electronic Health Records for Chronic Disease Surveillance. American Journal of Public Health, 107(6), 853-857.

Schiff, G. D., Volk, L. A., Volodarskaya, M., Williams, D. H., Walsh, L., Myers, S. G., ... & Rozenblum, R. (2017). Screening for medication errors using an outlier detection system. Journal of the American Medical Informatics Association, 24(2), 281-287.

Shankar, P. R., Kelleman, M. S., McCracken, C. E., Morris, C. R., & Simon, H. K. (2016, August). Real time access to online immunization records and its impact on tetanus immunization coverage in the ED. In Cognitive Computing and Information Processing (CCIP), 2016 Second International Conference on(pp. 1-6). IEEE.

Varonen, H., Kortteisto, T., & Kaila, M. (2008). What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians. Family Practice, 25(3), 162-167. https://dx.doi.org/10.1093/fampra/cmn020