Week 5 Discussions

docx

School

University Of Arizona *

*We aren’t endorsed by this school

Course

HIA601

Subject

Medicine

Date

Apr 3, 2024

Type

docx

Pages

Uploaded by CountPowerMoose43

Part 1 The Precision Medicine Initiative (PMI) Cohort Program is a research effort launched by the National Institutes of Health (NIH) in the United States. Its goal is to gather health information from one million or more volunteers to advance precision medicine, which tailors medical treatment to individual characteristics. In January of 2015, President Obama asked Congress to allocate 215 million dollars in funding for this initiative. Benefits: Some benefits of participating in the program include but are not limited to the advancement of personalized medicine where participants can contribute valuable data that can be used to develop personalized medical treatments based on individual genetic and environmental factors. An example of this is patient’s with cystic fibrosis which is a genetic disease that is caused by gene mutations. Another benefit of PMI is its contribution to science. The data collected from participants can help researchers and scientists better understand the links between genetics, lifestyle, and health outcomes, leading to advancements in medical research. Also, participants can gain potential access to health benefits like new treatments, preventative measures and even interventions from the research. Risks: On the other hand, some risks to using PMI include privacy concerns because there is a risk that mass collection of genetic information as well as personal health data could compromise individual privacy and lead to misuse of the information. Also, there is always a risk of data breaches or cyberattacks that could compromise the confidentiality of sensitive health information. Furthermore, learning about genetic predispositions or susceptibility to certain diseases may have psychological and emotional implications for participants and their families. Given this information, I would participate in this initiative. I have already done similar things such as 23 and me where I submitted my DNA to be compared to other samples to find relatives and risks for genetic diseases. I have also submitted a stool sample to Viome and got information regarding my microbiota and food aversions. I think giving companies access to this type of health information will continue to improve research and benefit people in the future. Because of the health information I received from these companies, I am better able to manage my own health. Rubin, R. (2015). Precision medicine: The future or simply politics? Journal of the American Medical Association, 313 (11), 1089–1091. https://doi.org/10.1001/jama.2015.0957 National Institutes of Health Precision Medicine Initiative Working Group. (2015). Precision Medicine Initiative cohort program – Building a research foundation for 21st century medicine . https://www.nih.gov/sites/default/files/research-training/initiatives/pmi/pmi-working-group-report- 20150917-2.pdf

Part 2 Clinical decision support systems applications include such things as evidence retrieval, computerized order entry, diagnostic assistance, and therapy planning. Select one clinical decision support systems application and compare and contrast two vendors using this application. Your comparison should include the similarities and differences in options provided between the two vendors and their use of technical support, as well as a critique of their website and supporting material. The content of the initial post must contain a minimum of two properly cited references (in APA Style as outlined in the Writing Center). Clinical decision support (CDS) encompasses a range of tools and interventions, both computerized and non-computerized. To fully realize the advantages of electronic health records and computerized physician order entry, it is crucial to have high-quality clinical decision support systems (CDSS), specifically those that are computerized. These systems can effectively consider all the data present in the electronic health record (EHR), enabling the identification of changes beyond the professional's usual scope and recognizing patient-specific alterations within normal limits. (Wasylewicz & Scheepers-Hoeks, 2018). Three vendors of EHRs are Epic Systems, Cerner, and Allscripts. Epic has a feature called SlicerDicer which is a reporting tool that can analyze advisory usage. The most common advisory in Epic is the Best Practice Alert (BPA). BPAs are displayed when a patient chart is opened and can include an alert because of a critical result or vital sign. Some examples are sepsis alerts, allergy checks, code status notifications, and CAUTI notifications (Rebhorn, 2023). In order to make the BPAs relevant, Epic applies the CDS 5 rights framework which includes the right information, to the right people, in the right intervention formats, through the right channels, at the right points in workflow. Allscripts, on the other hand, has a similar feature that also notifies the provider of the reason for the alert. However, according to McGinn et al. (2021), Epic and Allscripts both link their BPAs to order sets to make it more efficient to address the issue at hand. Cerner, on the other hand lacks this feature and the use of order sets is seldom found at healthcare facilities that are on Cerner. This could potentially decrease the quality of care. However, according to Oracle, Cerner, just like Epic, notifies providers of sepsis so that a rapid clinical decision can be made. Given the sources and research I was able to find on both Cerner and Epic systems, the two leading EHRs in the United States, it is evident that Epic is in the lead for a reason. Epic has an extremely iindepth website with current research on how different health networks around the world are able to improve their health outcomes and provider experiences. On the Oracle website, there is limited data and at the end there is a link to “Contact us to learn more”. This is not the case with Epic as there is so much data disposable to the public to learn about what their EHR is capable of.

McGinn, T., Feldstein, D. A., Barata, I., Heineman, E., Ross, J., Kaplan, D., Richardson, S., Knox, B., Palm, A., Bullaro, F., Kuehnel, N., Park, L., Khan, S., Eithun, B., & Berger, R. P. (2021). Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record. International Journal of Medical Informatics , 147 , 104349. https://doi.org/10.1016/j.ijmedinf.2020.104349 Oracle Cerner. Clinical decision Support. Retrieved from https://www.cerner.com/gb/en/solutions/clinical-decision-support Rebhorn, G. (2023). Taking a thoughtful approach to clinical decision support: The right information to the right person at the right time. EpicShare . Retrieved from https://www.epicshare.org/share-and-learn/juronghealth-bpa-overhaul Wasylewicz, A. & Scheepers-Hoeks, A. (2018). Clinical decision support systems. Fundamentals of Clinical Data Science. DOI: 10.1007/978-3-319-99713-1_11

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Week 5 Discussions

Related Documents