BMI 5760 Portfolio

Public Health Informatics Portfolio – Data

Healthcare-Associated Infections (HAIs) can increase mortality, length of stay, and cost in hospitalized patients. Generally, they may be prevented.   There are several types of healthcare-associated infections.  For example, the Centers for Disease Control and Prevention (CDC) states that HAIs include central line-associated bloodstream infections, catheter-associated urinary tract infections, and ventilator associated pneumonia.  Additionally, infections may occur at surgery sites.  It is estimated by the CDC  that “5-10% of patients inside hospitals acquire an HAI while studies showed that around 30-70% of HAIs can be prevented” (Hammound et al).  According to the World Health Organization, of every 100 hospitalized patients at a given time, 7 in developed and 10 in developing countries will acquire at least one healthcare-associated infection.  In addition, in high-income countries, approximately 30% of patients in intensive care units (ICU) are affected by at least one healthcare-associated infection.

Not only does a HAI negatively impact the patient and most importantly their health and safety, the presence of  HAIs may also impact resource utilization and the facility financially.  In addition, HAIs can prolong hospital stays and increase resistance to antibiotics. Involving the patient more in the care process could reduce the chance of obtaining a healthcare-associated infection.  This can be completed through patient empowerment, or patient involvement. Patient empowerment refers to “permitting patients to achieve the information and build the needed skills to make decisions and contribute to their care process by educating and encouraging them to participate in all aspects” (Hammound et al).    Since a vast majority of HAIs can be prevented, what measures may be implemented in order to reduce the amount of healthcare-associated infections at a facility in Ohio? If changes in protocols are implemented, how will that affect the HAI rate at that facility?

Hypothesis: If a facility is located in a rural area of Ohio, does that put that facility at a higher risk of being below the national benchmark in regards to hospital acquired infections (HAIs)? 

In order to identify facilities that may need assistance in implementing new protocols, the Healthcare Associated Infection (HAI) Measures – Provider Data was used.  This data was accessed from and measures are developed by the CDC and are collected through the National Healthcare Safety Network (NHSN).  This dataset provides information on infections that occur while the patient is in the hospital.   The dataset contains quantitative data – including the facility name, address, measure name, status compared to the national average, score, and the measure start and end date.  The metadata was updated on July 29,2020.  A limitation would be that only specific measures are collected, such as SSI – Colon Surgery and MRSA Bacteremia: Observed Cases.  In addition, the measures are compared to the national benchmark, however this dataset does not account for variables such as funding to the facility and availability of resources.

To identify facilities that are below the national benchmark, Tableau was used.  The most recent spreadsheet was imported into Tableau.  One downfall of this process is that if you do not update the data source, you could not be analyzing the most current information.  Alternatively, the site had a visualization tool in order to manipulate the data.  This pulls the dataset from the website, so to ensure the data is always current, it would be beneficial to pursue this route.  However, the visualization tool on the website has limitations and you can produce more in depth visualizations with Tableau.

Below are visualizations reflecting the facilities and their standing in comparison to the national benchmark.

This pie chart shows all of the facilities and the proportion that are better, worse and the same as the national benchmark.   The larger section of the pie chart are facilities (105,840)  that do not have data available.  There are 53,964 facilities (31%) that are no different than the national benchmark and 13,986 (8%) facilities that are better than the national benchmark. Only 2,034 facilities are worse than the national benchmark.  Below is a map of Ohio highlighting facilities that fall into these categories.  Going from left to right, facilities that are better than the national benchmark are displayed followed by no difference and lastly worse than the national benchmark.

Below is a map of Ohio that separates the counties based on the URGEO data from the FY2019 IPPS Final Rule published by the Centers for Medicare and Medicaid services (CMS).  This data set “contains the final FY 2019 readmissions payment adjustment factors under the Hospital Readmissions Reduction Program that will be applicable to discharges occurring on or after October 1, 2018. It also contains information on the number of cases for each of the applicable conditions and the base operating DRG payments used in the calculation of the readmission payment adjustment factors” (

Urban counties are denoted by a score of 0, or the light green on the map and rural counties are denoted by a score of 2, or navy blue on the map. 

It is apparent that the facilities that are better than the national benchmark are generally in higher populated areas of Ohio.  The facilities that are below the national benchmark are located in both urban and rural classified counties.  Since the facilities that do not differ are spread throughout the state, it would be beneficial to focus on those that are not in an urban area, as they are likely more at risk for dropping below the national benchmark – possibly due to lack of resources or funding.



FY 2019 Final Rule and Correction Notice Data Files. (n.d.). Retrieved October 10, 2020, from

Hammoud, S., Amer, F., Lohner, S., & Kocsis, B. (2020). Patient education on infection control: A systematic review. American journal of infection control, S0196-6553(20)30354-0. Advance online publication.

Healthcare-associated Infections (2016, March 4).  Retrieved from

Healthcare-associated Infections Fact Sheet.  Retrieved from


Public Health Informatics Portfolio – Survey

The purpose of this survey is to evaluate how knowledgeable facilities are about hospital acquired infections and to see if during a patient’s healthcare stay, staff at the facilities  are completing tasks – before, during and after procedures – to help reduce the chance of obtaining a hospital acquired infection. In addition, this survey will evaluate what safety measures are put in place to help reduce the chance of a patient acquiring a hospital acquired infection.  Since our data is is evaluating if the location of the hospital – rural or urban – and the impacts of the rate of hospital acquired infections at the facility, the survey will ask participants to identify the zip code and county of the healthcare facility.
The target audience of this survey is clinical and non-clinical healthcare workers, ranging from nurses to hospital administrators in all departments of the facility.  Having this range in responses is imperative, as it is good to consider opinions and feedback from those who create policies (hospital administrators) and those who deliver patient care (nurses and physicians).  This survey will be administered electronically via a link to the respondents email with an identifier being the respondents occupation.  A challenge that could be that the responses by the respondents could not be honest.  For example, this survey is asking questions like “do you wash your hands every time you enter and exit a patient’s room”.  If the respondent does not do what the question asks, they may not answer truthfully.

Link to Survey:

Some challenges that came with developing the survey was making sure you can capture all of the necessary information for your study while making sure that the survey is not too long or repetitive.  In addition, choosing the format of the survey was a challenge.   You want to make sure you capture the information in a way that is easy to analyze.  For example, a free-text section would allow respondents to adequately express their opinion about the subject, however it is hard to effectively analyze.  If you were to have a free-text section, you would have to train those analyzing the survey to ensure the results are consistent.