Feb 23, 2024 DNP 805 Week 4 Assignment EHR Database and Data Management
DNP 805 Week 4 Assignment EHR Database and Data Management
DNP 805 Week 4 Assignment EHR Database and Data Management
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Introduction
Prostate cancer is the most frequent sickness among males, according to medical studies on cancer therapy (Hernandez-Boussard, Blayney & Brooks, 2020). Recently diagnosed males face a plethora of treatment options, each with varying risks of obtaining patient-centered results such as urinary and erectile problems. Currently, care providers and patients find it challenging to compare the trade-offs among patient-centered outcomes across diverse treatments since experimental evidence on these trade-offs is lacking (Hernandez-Boussard, Blayney & Brooks, 2020). Experts believe this is because patient-centered outcomes are not regularly recorded in computer-readable formats. In order for healthcare facilities to improve cancer care and data quality, evidence recorded in computable forms should be made available to physicians and patients via a Web-based platform (Hernandez-Boussard, Blayney & Brooks, 2020). This article proposes three key revolutionary measures for the administration of cancer patient data.
The first proposed solution advocates for the establishment of an EHR prostate cancer database, which will allow clinical data to be weighed alongside diagnostic information (Hernandez-Boussard, Blayney & Brooks, 2020). The second method creates new ontologies of quality metrics that are accessible and consistent across EHR applications. The third suggested strategy is to create a strong data information mining workflow that is based on modern methodologies and uses ontology-based dictionaries to parse free text (Hernandez-Boussard, Blayney & Brooks, 2020). By combining these three novel approaches, physicians and patients will be able to effectively use current EHRs to understand the trade-offs between patient-centered outcomes across various treatments.
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Using EHR to measure and improve prostate cancer therapy is critical because it allows healthcare facilities to share critical information (Hernandez-Boussard, Blayney & Brooks, 2020).
It is simple to establish the building blocks required to recognize quality metric information in EHRs using the EHR. The creation of an EHR database, mapping of quality measurements to medical vocabularies, and development of electronic quality metric phenotypes are all critical. The EHR initiative develops a web-based application that incorporates empirical information and clinical characteristics to assess patient tailored risk prediction, assisting care providers and patients in making treatment decisions (Hernandez-Boussard, Blayney & Brooks, 2020). Given the importance they place on each patient-centered result, these alternatives deliver the best-anticipated quality of care. Using EHR will help to close a critical gap in evidence for prostate cancer therapy and research by providing care professionals and patients with useful evidence to compare the trade-offs between patient-centered outcomes across various treatments.
Details
As a DNP-prepared nurse, you may be called upon to assist in the design of a clinical database for your organization. This assignment requires you to integrate a clinical problem with data technologies to better understand the components as well as how those components can lead to better clinical outcomes.
General Guidelines:
Use the following information to ensure successful completion of the assignment:
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
This assignment requires that at least two additional scholarly research sources related to this topic, and at least one in-text citation from each source be included.
You are required to submit this assignment to Turnitin. Refer to the directions in the Student Success Center.
Thank you. I chose the elements such the time, date , numbers, and text because these elements are specifically unique to such population, pregnant migrants or not. As I mentioned in my post, every one of these elements should not be treated individually instead treat them as a whole. These elements are valuable in the database and should not be single out from the others in the system as each of these share a piece of the bigger puzzle (Colvin et al., 2013). For instance, the time comprises the exact time a pregnant patient seen by obstetrician either in the clinic or hospital. It can also pertains to the exact time of delivery. It can also be the time when a certain procedure starts lust like in caesarean section. If there is a time element, there should be the date as well and this should match and observed properly. The other element is about the numbers, it pertains to the OB/GYN history like how many times that patient being pregnant, if there is missed abortion, or number of pregnancy that reached full term. It can also indicate the fundal height when measured, or even the date of birth of the patient, and other things that will result to numbers. I also chose text because, these are the words and phrases that the pregnant patient answered to certain questions. I also choose this element because it actually pertains with the whole interaction between the patient and healthcare staff.
Colvin, C., Baird, P., Easty, T., & Trbovich, P. (2013). Human Factors and Medical IT Systems: Complex Incident Reporting Systems and Multiple IV Infusions.Biomedical Instrumentation & Technology, 47(2), 59-63. https://lopes.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/human-factors-medical-systems-complex-incident/docview/1461394405/se-2?accountid=7374
Directions:
For this assignment, write a 1,000-1,250 word paper in which you:
Select a clinically-based patient problem in which using a database management approach provides clear benefit potential.
Better clinical outcomes and patient satisfaction are the most important things that every clinician looks forward to. As a doctor of nursing practice prepared nurse, one can be called upon in assisting with the designing of a clinical database of their organization for a better clinical outcome. Therefore, the purpose of paper is to, “identify a patient clinical problem in which using a database management approach provides clear benefit potential, Identifying the data needed to manage this patient problem using information from the electronic health record (EHR), identifying whether the data is structured or unstructured and providing a complete description of the structured and unstructured data from the EHR that are needed to organize a hypothetical database” (GCU, 2017).
The Clinical problem
Predicting Sepsis Risk and mortality is a clinical problem that can be managed by data from EHRs to provide clear benefit potential. According to Miller, (2016), “Physicians are forever recording information about their patients. They take vital signs, order lab tests and imaging, prescribe medications, check boxes to define patients’ diagnoses for billing purposes, and write or dictate narrative descriptions of each patient’s status.” All this data is found in structured and unstructured data. “The widespread adoption of electronic health records by US health care providers is motivating a rapid growth in the use of predictive models to guide clinical decisions, to identify patients at high risk of future events (e.g., 30-day readmission), and to detect disease early, among other applications” (Dey, et al., 2016). This data, structured or unstructured is used in predicting sepsis in its early stages, which has been found to be one of the “leading cause of death and hospitalization in the United States” (Dey, et al., 2016).
According to Desautels, et al. (2016), “Sepsis is defined as a systemic inflammatory response syndrome (SIRS) due to infection.” They go on to explain that, “Sepsis, severe sepsis, and septic shock are umbrella terms for a broad and complex variety of disorders characterized by a dysregulated host response to infectious insult and because of the heterogeneous nature of possible infectious insults and the diversity of host response, these disorders have long been difficult for physicians to recognize and diagnose” (Desautels et al, 2016). The criteria for SIRS is having, “Temperature >38°C or <36°C, Heart Rate >90 bpm, Respiratory Rate >20 Breaths Per Minute, or Arterial carbon dioxide tension <32 mm Hg (equivalent to 4.3 kPa) and White Blood Cell Count >11 or <4 (×109 cells), or 10% immature (band) forms” (Desautels et al, 2016). Also measuring three elements, “lactate level, blood pressure and respiratory rate can pinpoint the likelihood that a patient will die from the disease”. There are several bedside scoring systems that can help nurses and doctors to predict sepsis so that an early intervention helps to prevent morbidity and mortality in these patients. Some of these bedside scoring systems are:
“InSight
qSOFA (quick SOFA)
Sequential Organ Failure Assessment (SOFA) score
Modified Early Warning Score (MEWS)
Simplified Acute Physiology Score (SAPS II)
Systemic Inflammatory Response Syndrome (SIRS) criteria” (Desautels et al, 2016).
AutoTriage “AutoTriage is designed to detect imbalances in homeostasis through the analysis of correlations between patient vital signs and clinical measurements over time. AutoTriage is designed to continuously sample and analyze patient measurement correlations automatically, and be able to alert clinicians to a deteriorating patient’s state” (Calvert J., et al., 2016).
Identify the data needed to manage this patient problem using information from the electronic health record (EHR).
Include a brief description of the patient problem which incorporates information needed to manage the specific problem.
Identifies whether the EHR-supplied data is structured or unstructured with an explanation as to why.
Provide a complete description of the structured and unstructured data from the EHR that are needed to organize a hypothetical database.
Provide a complete description of data relationships that apply to the hypothetical database.
DNP 805 Week 4 Assignment EHR Database and Data Management
Assessment Description
As a DNP-prepared nurse, you may be called upon to assist in the design of a clinical database for your organization. This assignment requires you to integrate a clinical problem with data technologies to better understand the components as well as how those components can lead to better clinical outcomes.
A data type “is a set of values that have similar characteristics” (Nakov, & Kolev, 2013, p111). There are various types of data types are specific data types are used dependent on the information that the writer wishes to convey and how it will be stored in a database. Data types are used in a data base in the format of tables, forms and reports to generate records. In this database involving a patient over 65 years who is a diabetic, the following data type would be beneficial to assist in organizing the client’s information in columns and tables in the database:
Integers: This refers to numerical data that has no fractions (Chapple, 2008). The patient’s age expressed as 65 would be an integer. Blood sugar in mg/dl would be expressed as integer.
Float: These are numbers that are expressed with fractions (Chapple, 2008). Insulin dosage can be expressed in a database using floats, for example Novolin R 0.2 units. Or antibiotic Zosyn 3.375 GM. An address can be expressed as a float such as some address still use fractions, for example 12 ½ Main Street. The patient’s height is also expressed as float for example 5ft 7 ½ inches but in cm; 175.2 cm. Weight is also expressed as float. Body Mass Index (BMI) is also expressed as float. Body Surface Area (BSA) is also expressed as float. Blood sugar in mmol/l would be expressed as float.
Boolean: This represent values that are expressed as true or false (Busbee, & Braunschweig, nd). In the database for this diabetic patient, any questions that needs to be expressed using true or false, Boolean would be used for example, nutrition questionnaires. Also, some of the questions in the patient’s history that requires true or false answers Boolean would be used.
Date: The date is important in any date base and is usually stored as year, month day (Chapple, 2008). This in the database for the diabetic would be capture as the date of admission or the date of discharge or first onset of illness.
Time: This is usually expressed in hours minutes and seconds (Chapple, 2008). For example, time of admission, time of discharge. Date and time are sometimes stored in databases as combined values.
String (text): Sometimes called a string of character. This is usually a series of character or a linking of number or symbols that has no mathematical functions (Busbee, & Braunschweig, nd). The patient’s name would be expressed as a string. Address can also be expressed as a string. City, state, Zip code. The phone number and fax number can be expressed as string. In the patient’s history, string can also be used to capture the clinical data. Marital status would also be expressed as a string. Ethnicity is also expressed as string.
Character: These are single expressions and are usually symbols, digit, blank space, or punctuation mark (Busbee, & Braunschweig, nd). What percentage of diabetics in your database are taking insulin? The result would be expressed as a character if the number is single example 1 %.
The demographic variables of the clinical data element in a diabetic such as name, age, gender, weight, height, BSA, BMI, marital status, job and residential address and patient contact number of this diabetic patient would be expressed in the database as string, integer, string if the word is written out or character if the letter representing the gender is used, float, float, float, float, character, string, string, string respectively and would fall in columns within a table. The clinical data including the patient’s history and question concerning health in response to diabetes would be captured in a database for example how many known diabetics were admitted on the unit, where was the patient admitted from, type of diabetic, type of diabetic medication whether an oral agent or insulin, other comorbidities and length of hospital stay would be captured within column in a table and linked to the database for easy access and careful analysis when needed. Information when organized correctly can be interpret in a database about the diabetic patient over 65 years with specific comorbidity for optimal treatment and improve care.
References
Busbee, K, L., & Braunschweig, D. (nd). Programming Fundamentals: A Modular Structured Approach (2nd Ed). Press book. https://press.rebus.community/programmingfundamentals/chapter/data-types/#footnote-117-1
Chapple, M. (2008). Microsoft SQL server 2008 for dummies. John Wiley & Sons, Incorporated. https://ebookcentral-proquest-com.lopes.idm.oclc.org/lib/gcu/reader.action?docID=380427&query=types+of+data+and+elements+in+a+database
Nakov, S., & Kolev, V. (2013). Fundamentals Of Computer Programming with C#. Faber Publishing: https://introprogramming.info/wp-content/uploads/2013/07/Books/CSharpEn/Fundamentals-of-Computer-Programming-with-CSharp-Nakov-eBook-v2013.pdf
General Guidelines:
Use the following information to ensure successful completion of the assignment:
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
Use primary sources published within the last 5 years. Provide citations and references for all sources used.
Refer to the examples in the topic resources for health care database examples.
You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
Learners will submit this assignment using the assignment dropbox in the learning management system. In addition, learners must upload this deliverable to the Learner Dissertation Page (LDP) in the DNP PI Workspace for later use.
Directions:
For this assignment, write a 1,000-1,250 word paper in which you:
Select a clinically based patient problem in which using a database management approach provides clear benefit potential.
Consider how a hypothetical database could be created to assist with this clinically based patient problem. Identify and describe the data needed to manage this patient problem using information from the electronic health record (EHR).
Include a brief description of the patient problem that incorporates information needed to manage the specific problem. Describe what information is required for the patient to manage the condition and how the database and health care provider can be incorporated into the approach for better health outcomes.
Describe each entity (data or attribute) that will be pulled from the EHR as either structured or unstructured and provide an operational definition for each. Structured data is more easily searchable and specifically defined. For example, structured data can be placed in a drop-down menu like hair color: brown, black, grey, salt and pepper, blonde, platinum, etc. Unstructured data is data that would be included in a nurse’s notes. An operational definition is how a researcher or informatics specialist decides to measure a variable. For example, when the nurses enter height into the EHR, do they enter height as measured in inches or centimeters or in feet and inches?
Provide a complete description of data entities (the objects for which you seek information, e.g., patients) and their relationships to the attributes collected for each entity (data collected for each entity, e.g., gender, birthdate, first name, last name) that apply to the hypothetical database. You can use a concept map similar to the “Database Concept Map” resource, to help you describe the relationships between each entity and its attributes.
DNP 805 Week 4 Assignment EHR Database and Data Management
Attachments
DNP-805A-RS-DatabaseConceptMap.docx
Portfolio Practice Hours:
It may be possible to earn portfolio practice hours for this case report. Enter the following after the references section of your paper:
Practice Hours Completion Statement DNP-805
I, (INSERT NAME), verify that I have completed (NUMBER OF) clock hours in association with the goals and objectives for this assignment. I have also tracked said practice hours in the Typhon Student Tracking System for verification purposes and will be sure that all approvals are in place from my faculty and practice mentor.
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Rubric Criteria
Total100 points
Criterion
1. Unsatisfactory
2. Less Than Satisfactory
3. Satisfactory
4. Good
5. Excellent
Mechanics of Writing (includes spelling, punctuation, grammar, language use)
Mechanics of Writing (includes spelling, punctuation, grammar, language use)
0 points
Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.
4 points
Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.
4.4 points
Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.
4.6 points
Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.
5 points
Writer is clearly in command of standard, written, academic English.
Patient Problem Description Incorporates Information Needed to Manage the Problem
Patient Problem Description Incorporates Information Needed to Manage the Problem
0 points
Patient problem description that incorporates information needed to manage the problem is not present.
12 points
Patient problem description that incorporates information needed to manage the problem is marginal or incomplete.
13.2 points
Patient problem description that incorporates information needed to manage the problem is present but at a perfunctory level.
13.8 points
Patient problem description that incorporates information needed to manage the problem is present in full. Discussion is convincing and defines specific elements. Information presented is from scholarly though dated sources.
15 points
Patient problem description that incorporates information needed to manage the problem is present in full. Discussion is convincing and defines specific elements. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.
Documentation of Sources
Documentation of Sources (citations, footnotes, references, bibliography, etc., as appropriate to assignment and style)
0 points
Sources are not documented.
4 points
Documentation of sources is inconsistent or incorrect, as appropriate to assignment and style, with numerous formatting errors.
4.4 points
Sources are documented, as appropriate to assignment and style, although some formatting errors may be present.
4.6 points
Sources are documented, as appropriate to assignment and style, and format is mostly correct.
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