Electronic Health Record (EHR)

Public health agencies and researchers have traditionally relied on the Behavioral Risk Factor Surveillance System (BRFSS) and similar tools for surveillance of non-reportable conditions. These tools are valuable but the data are delayed by more than a year, limited in scope, and based only on participant self-report. These characteristics limit the utility of traditional surveillance systems for program monitoring and impact assessments. Automated surveillance using electronic health record (EHR) data has the potential to increase the efficiency, breadth, accuracy, and timeliness of surveillance. We sought to assess the feasibility and utility of public health surveillance for chronic diseases using EHR data using MDPHnet. MDPHnet is a distributed data network that allows the Massachusetts Department of Public Health to query participating practices’ EHR data for the purposes of public health surveillance (www.esphealth.org). Practices retain the ability to approve queries on a case-by-case basis and the network is updated daily.

Objective

To assess the feasibility of tracking the prevalence of chronic conditions at the state and community level over time using MDPHnet, a distributed network for querying electronic health record systems

Since 2009, Houston Health Department (HHD) uses an electronic disease surveillance system (Maven) to receive ELRs from reporting facilities in the Houston jurisdiction. Currently, two large hospital systems, a blood bank, two large commercial labs, and two public health labs are sending ELRs to Maven. The overall percentage of disease reports received via ELR was over 50%. We hypothesize that the implementation of ELR has improved the timeliness and completeness of disease surveillance.

Objective

Review 5 years of surveillance data post electronic lab reporting (ELR) implementation and 8 years of data prior to ELR, to evaluate timeliness and completeness of disease surveillance.

Most European countries are facing a continuous increased influx of asylum seekers. Poor living conditions in crowded shelters and refugee camps increase the risk for - outbreaks of - infectious diseases in this vulnerable population. In line with ECDC recommendations, we aim to improve information on infectious diseases among asylum seekers by establishing a new syndromic surveillance system in the Netherlands. This system will complement the notifiable disease system for infectious diseases.The aim of the syndromic surveillance system is to improve the detecting of outbreaks of infectious diseases in asylum seekers’ centres in an early stage of development to be able to take adequate and timely measures to prevent further spread, and to collect information on the burden of infection within this population.

Objective

Facing challenges to establish a new national syndromic surveillance system in the Netherlands for infectious diseases among asylum seekers.

Under the CDC STD Surveillance Network (SSuN) Part B grant, WA DOH is testing electronic case reporting (eCR) of sexually transmitted infections (STI) from a clinical partner.

Objective

We reviewed CCDs (a type of consolidated clinical data architecture (C-CDA) document) shared by our clinical partner, Planned Parenthood of the Great Northwest and Hawaiian Islands (PPGNHI) since October, 2015. Analyses focuses on:

-Completeness

-Degree to which the CCD matches program area information needs

-Differences in EHR generation methods

-Presence and location of triggers (based on the Reportable Conditions Trigger Codes) that would initiate CCD generation.

Once a facility meets data quality standards and is approved for production, an assumption is made that the quality of data received remains at the same level. When looking at production data quality reports from various states generated using a SAS data quality program, a need for production data quality assessment was identified. By implementing a periodic data quality update on all production facilities, data quality has improved for production data as a whole and for individual facility data. Through this activity several root causes of data quality degradation have been identified, allowing processes to be implemented in order to mitigate impact on data quality. 

Objective

To explore the quality of data submitted once a facility is moved into an ongoing submission status and address the importance of continuing data quality assessments. 

EMRs are a potentially valuable source of information about a patient’s history of health risk behaviors, such as excessive alcohol consumption or smoking. This information is often found in the unstructured (i.e., free) text of physician notes. It may be difficult to classify and analyze health risk behaviors because there are no standardized formats for this type of information1. As well, the completeness of the data may vary across clinics and physicians. The application of automated classification tools for this type of information could be useful for describing patterns within the population and developing disease risk prediction models.

Natural Language Processing (NLP) tools are currently used to process EMR free text in an automated and systematic way. However, these tools have primarily been applied to classify information about the presence or absence of disease diagnoses. The application of NLP tools to health risk behaviors, particularly alcohol use information from primary care EMRs, has thus far received limited attention. 

Objective

The research objective was to develop and validate an automated system to extract and classify patient alcohol use based on unstructured (i.e., free) text in primary care electronic medical records (EMRs).

There is a resurgence in the need to evaluate the economic burden of prescription drug hospitalizations in the United States. We used the Wisconsin 2014 Hospital Discharge data to examine opioid related hospitalization incidence and costs. Fentanyl, a powerful synthetic opioid, is frequently being used for as an intraoperative agent in anesthesia, and post-operative recovery in hospitals. According to a 2013 study, synthetic Fentanyl is 40 times more potent than heroin and other prescription opioids; the strength of Fentanyl leads to substantial hospitalizations risks. Since, 1990 it has been available with a prescription in various forms such as transdermal patches or lollipops for treatment of serious chronic pain, most often prescribed for late stage cancer patients. There have been reported fatal overdoses associated with misuse of prescription fentanyl. In Wisconsin number of total opioid related deaths increased by 51% from 2010 to 2014 with the number of deaths involving prescription opioids specifically increased by 23% and number of deaths involving heroin increased by 192%. We hypothesized that opioids prescription drugs, as a proxy of Fentanyl use, result in excessive health care costs.

Objective

In this paper we used hospital charges to assess costs incurred due to prescription drug/opioid hospitalizations