DNP 805 Topic 4 DQ 1 Select a defined patient population; for example, diabetic patients over 65 years of age
DNP 805 Topic 4 DQ 1 Select a defined patient population; for example, diabetic patients over 65 years of age
DNP 805 Topic 4 DQ 1 Select a defined patient population; for example, diabetic patients over 65 years of age
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DNP 805 Topic 4 DQ 1 Select a defined patient population; for example, diabetic patients over 65 years of age
Topic 4 DQ 1
May 5-7, 2022
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Select a defined patient population; for example, diabetic patients over 65 years of age. List elements that you think will be valuable in a database. Describe each element and explain each term of its type of data (text, numbers, date, time, binary, etc.). If an element can be more than one type of data, explain how that is possible. Be sure to post an element different from that of your peers.
REPLY TO DISCUSSION
Databases are a type of information collection that is related to and linked to the things that are being evaluated. In the twenty-first century, they have become an essential component of our daily lives, whether logical or physical. There are databases for various fields, and the information collected varies according to the field. This information could be gathered in a variety of formats, such as spreadsheets, files, indexes, and tables (Alexander, Hoy, & Frith, 2019). Understanding how databases work in terms of structure, design, management, application, and data storage is required for database implementation.
Databases are used in healthcare to store massive amounts of data such as demographics, diagnoses, treatment plans, patient care plans, medications, and care progressions, as well as to respond to complex inquiries. These databases are simple to recover data from and are rapidly improved and updated for faster access, allowing HCP to easily exchange information (Alexander, Hoy, & Frith, 2019).
The patient population chosen for this database is heart failure. Heart failure is a syndrome characterized by increased heart pressures or decreased cardiac output when the heart’s systolic and diastolic functions fail, resulting in symptoms such as constant shortness of breath, Bilat lower extremity edema, fatigue, and the possibility of needing to use oxygen continuously (Knc, & Gürdoan, 2022).
Some of the elements needed for chronic heart failure will be demographics which will include the age-number, gender-text, vital
DNP 805 Topic 4 DQ 1 Select a defined patient population; for example, diabetic patients over 65 years of age
signs-numeric, intake and output-numeric, weight gain or loss-numeric, medications-text, frequency of shortness of breath-text and numeric, time of day-time, when short of breath occurs most-text and numeric (Newman, 2019).
The frequency of sob could be text because you are noting that there is frequency of sob and then also noting how many times it occurs. Likewise, when the shortness of breath occurs most can be text as well as at what time it occurs most often.
References:
Alexander, S., Hoy, H., & Frith, K. (2019). Applied clinical informatics for nurses (2nd ed.). Jones & Bartlett Learning.
Kınıcı, E., & Gürdoğan, E. P. (2022). Hopelessness, Health Behaviors, and Quality of Life in Patients with Chronic Heart Failure. Journal of Education & Research in Nursing / Hemsirelikte Egitim ve Arastirma Dergisi, 19(1), 49–55. https://doi-org.lopes.idm.oclc.org/10.5152/jern.2022.79745
Newman, D. (2019). Healthcare database concepts – Databases in healthcare. Healthcare IT Skills, Health Information Technology Career Advice, Healthcare IT Certifications, Project Management, Job Tips. https://healthcareitskills.com/databases-in-healthcare-database-concepts/
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REPLY
Good post. Of the elements you discussed, why did you choose the ones you did?
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REPLY
Hello Dr. Gabua, I choose these elements to focus more on them and also because they are some of the elements that I see displayed by my mother who has chronic heart failure though on a mild scale. She is not in these statistics yet of being depressed and hopeless because of the frequent hospitalizations and duration of hospital stay which would lead to a higher mortality rate. Thank God that I am there to keep an eye on her and support her. She is on a regimen of medications for the heart, blood pressure and water pill. She has to perform minimum to moderate exercises as she can tolerate and monitor her urine output. She has to adjust and make some lifestyle changes which we all have to follow her to adjust to make it easy on her and myself when we cook. Like monitoring the salt and water intake and output as well as the color of the urine. Thank God that she is not living alone, I am there to support her. Also, research notes that the symptoms of HF and depression overlap, so it is difficult to pinpoint the cause of the symptom with a depression screening. The two conditions have similar symptoms which include weakness, reduced physical activity, lack of energy, weight gain or loss, sleep pattern changes and cognitive decrease or concentration. It is noted that exercise therapy is safe for the chronic HF patients because it helps to decrease the symptoms of depression and increase their quality of life and there is no medication drug-drug interaction (Wilhelm, Davis, Sharpe, & Waters, 2022).
Reference:
Wilhelm, E. A., Davis, L. L., Sharpe, L., & Waters, S. (2022). Assess and address: Screening and management of depression in patients with chronic heart failure. Journal of the American Association of Nurse Practitioners, 34(5), 769-779. https://doi.org/10.1097/jxx.0000000000000701
REPLY
Thank you for your post, I agree with you that databases are used to store large quantity of data, such as for record keeping of patient information. Databases in healthcare sectors provide a proper system for storing, organizing, and managing critical health statistics such as labs, finances, billing and payments, patient identification, and more. This information must remain confidential to the public, but easily accessible for the healthcare professionals who use this data to save lives. The importance of database technology in healthcare cannot be overstated, it’s crucial for doctors, providers, and management teams to access in-depth health data quickly and without error. Healthcare operations, from large-scale to individual processes, depending on the accuracy and efficiency of healthcare databases. A healthcare database management system is an essential tool for databases in healthcare industries (Newman, D. 2019).
Databases used in the healthcare industry can store loads of information and can assist with several tasks, including the most important healthcare mission of saving lives. Along with supporting the daily operations of healthcare professionals, databases must also be efficient so that healthcare professionals can quickly and easily access relevant information when necessary.
Database performance to healthcare is important and affected by the data storage and capability to adopt any scenario. Data integration is present and can facilitate patient health information transfer and sharing to other clinicians. There is growing interest in using data captured in electronic health record (EHR) for patients record. Clinical information are designed to and used for different purposes. It is to help the system to manage the clinical workflow and improve efficiency and achieve quality improvement and patient care.
Public health emergency such as COVID-19 pandemic is one example of which data is used and was accessed appropriate information to policy makers that created all the policies and procedures to be implemented. Keeping the public safe by using collective dat and knowledge for preparedness. EHR contain many important data elements that can help pandemic response to limit shortcomings and utilize records to provide critical answers and care to the public. There is a data that was created for virtual meetings/appointments for patients. Efforts should continue to decrease the gaps. Public health surveillance and reporting systems, disease registries, and patient-reported data is crucial. Support public health data and the tools that are being used.
References:
Brat, G. A., Weber, G. M., Gehlenborg, N., et al. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ Digit med 2020.
Kannampallil, T. G., Foraker, R. E., Lai, A. M. Woeltje KF, Payne, P. R. O. When past isn’t a prologue: adapting iformatics practice during a pandemic. J Am Med Inform Assoc. 2020.
O’Reilly- Shah, V. N., Gentry, K. R., Van Cleve W., Kendale, S. M., Jabaley, C. S., Long D. R. The COVID-19 pandemic highlights shortcomings in US Health care informtics infrastructures: a call to action. Anesth Analg2020; 131.
Databases are useful to organize, analyze and store information regarding various health data. This information can be used to search for trends, assess quality, track healthcare usage, and allow for information exchanges (Databerry, 2022). For databases to provide useful information, elements need to be carefully considered. This quantitative information can be pulled from the electronic health record. Databases can be helpful in tracking and trending disease processes such as Obstructive Sleep Apnea (OSA).
OSA is a common diagnosis with a series of serious sequelae that can follow. Clearly defining the population is the first step in constructing a database. OSA can affect adults and children. This database would be constructed to evaluate obstructive sleep apnea for patients 18 years and older. Various types of elements will be pertinent to this disease process.
Demographic data describes information about a person. In this database, age, smoking history, BMI, family history of OSA, and occupation would be helpful information to collect. Patients with OSA tend to have a higher BMI, smoking history and may have exposure to occupational hazards. This would be valuable in the obstructive sleep apnea database. Comorbidities would be important to examine. People with OSA have a higher risk of major depression, and metabolic or cardiovascular disease (Hein et al., 2017).
Boolean data is comprised of one or two values, typically, true or false (Busbee & Brauschweig, n.d.). Smoking history, metabolic and cardiovascular disease, or depression could be a Boolean type of value. Use of a CPAP could be a Boolean type of data as well. Respiratory medications could be represented as a percentage or numerical type of data. Age at the time of diagnosis would be a valuable piece of data to include in the database as well. Having the address or location may help assess for prevalence. This data would be string text. Ferritin and CRP can be altered as well due to OSA (Hein et al., 2017). Lab scores of ferritin and CRP would be numeric data along with hours of sleep. Through careful selection and incorporation, various data points can be pulled for patients with OSA. Depending on how data is to be reported, many elements, as discussed, can be pulled into different types of data points.
References
Busbee, K, L., & Braunschweig, D. (nd). Programming Fundamentals: A Modular Structured Approach (2nd Ed). Pressbook. https://press.rebus.community/programmingfundamentals/chapter/data-types/#footnote-117-1
Databerry. (2022). Why are Databases Important in Healthcare? Retrieved on May 5th, 2022 from https://databerry.com/news/why-are-databases-important-in-healthcare/
Hein, M., Lanquart, J. P., Loas, G., Hubain, P. & Linkowski, P. (2017). Prevalence and risk factors of moderate to severe obstructive sleep apnea syndrome in major depression: a observational and retrospective study on 703 subjects. BMC Pulmonary Medicine, 17, 165. Doi: https://doi.org/10.1186/s12890-017-0522-3
Hello Angela. Thank you for sharing your discussion post about the data elements needed in patients with obstructive sleep apnea (OSA) and the importance of such data in research. Indeed, despite many concerns in relation to patient privacy, confidentiality, and awareness, electronic health record (EHR) databases are a rich source of research data. Research done on data from these databases is vital in improving the diagnosis and treatment of diseases (Brelsford et al., 2018). I would also like to add to the discussion that the past medical and social history could be classified as a string data type, which allows one to enter a combination of alphabetic, numerical, and special character data (Carter, 2018). This implies that the string data type can enable a clinician to record details of an OSA patient, such as the date of diagnosis, duration of the disease, and circumstances surrounding the diagnosis, such as treatment or complications.
References
Brelsford, K. M., Spratt, S. E., & Beskow, L. M. (2018). Research use of electronic health records: patients’ perspectives on contact by researchers. Journal of the American Medical Informatics Association: JAMIA, 25(9), 1122–1129. https://doi.org/10.1093/jamia/ocy087
Carter, P. A. (2018). SQL server advanced data types: JSON, XML, and beyond. New York: Springer Science
The electronic database assists in identifying valuable information regarding certain patient populations. In the emergency setting, the database has descriptors and questionnaires that correlate to chief complaints of patients that arrive. For example, patients who arrive with possible stroke symptoms will engage in not only an extensive neurological assessment (NIHSS and GCS scores) but the elements that also include date, time, age, vital signs, medical and surgical history, lab values and diagnostic tests. These Common Data Elements (CDE) can easily be pulled for research and quality improvement purposes and can assist organizations in data collection, data sharing, and data quality improvement (Project Overview, n.d.)
The date and time of onset of symptoms are needed to establish if the patient may receive tissue plasminogen activator (tPA). These times coincide with the ER arrival as the ER is also required to meet certain criteria within certain time frames.
Age plays a particular numeric role in identifying the risk of stroke along with medical and surgical history. A patient who is healthy and in their 20’s may not be in the high-risk category as a patient with a history of cardiac disease in their 80’s.
Additional numeric values such as vital signs, lab values, Glasgo Coma Scale (GCS) and National Institutes of Health Stroke Scale (NIHSS) assist in quantifying the risk of stroke along with the risk of deterioration due to the patient’s presentation.
The data collected within the database assists with developing a clear diagnosis and plan of care. Using such elements as the NIHSS has shown a high predictive value for an acute stroke. Furthermore, researchers have found that the NIHSS can identify the degree of neurological impairments and can also predict prognosis (Zhao et.al, 2018). The implementation of data elements into the EHR has made information easily accessible for further use in treatment.
U.S. Department of Health and Human Services. (n.d.). Project overview. National Institute of Neurological Disorders and Stroke. Retrieved May 5, 2022, from https://www.commondataelements.ninds.nih.gov/ProjReview#:~:text=Examples%20of%20the%20General%20CDEs,by%20participants%20throughout%20a%20study.
Zhao, X.-J., Li, Q.-X., Liu, T.-J., Wang, D.-L., An, Y.-C., Zhang, J., Peng, Y.-B., Chen, R.-Y., Chang, L.-S., Wang, Y., Zhang, L., Fan, H.-Y., Wang, X.-J., & Zheng, F.-X. (2018). Predictive values of CSS and NIHSS in the prognosis of patients with acute cerebral infarction. Medicine, 97(39). https://doi.org/10.1097/md.0000000000012419
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