DNP 805 Assignment EHR Database and Data Management Essay

DNP 805 Assignment EHR Database and Data Management Essay

DNP 805 Assignment EHR Database and Data Management Essay

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According to medical studies in regards to cancer treatment, Prostate cancer is regarded as the most common illness among men (Hernandez-Boussard, Blayney & Brooks, 2020). Recently diagnosed men encounter complex therapy choices, each with various risks of obtained patient-centered outcomes such as urinary and erectile dysfunction. In these present times, care providers and patients find it difficult to contrast the trade-offs among patient-centered results across various treatments since the experimental evidence about these trade-offs does not exist (Hernandez-Boussard, Blayney & Brooks, 2020). According to experts, this is because patient-centered outcomes are not consistently recorded in computable formats.  For healthcare institutions to enhance cancer care and accuracy of data, evidence recorded in computable forms should be placed in the hands of the clinicians and patients through a Web-based tool (Hernandez-Boussard, Blayney & Brooks, 2020).  There are three significant innovative measures proposed in this article concerning the management of cancer patient’s data.

The first proposed approach endorses the development of an EHR prostate cancer database that will create an opportunity for clinical information to be analyzed alongside diagnostic details (Hernandez-Boussard, Blayney & Brooks, 2020).  The second approach creates new ontological representations of quality metrics that are public and reliable across the EHR programs. The third proposed approach involves gathering a robust data information mining workflow that expands on modern methods by centering on ontology-based dictionaries to interpret the free text (Hernandez-Boussard, Blayney & Brooks, 2020). Combining these three innovative approaches will uniquely allow both clinicians and patients to use current EHRs to understand the trade-offs among patient-centered outcomes across various treatments adequately.

Utilizing EHR to measure and enhance prostate cancer treatment is essential since it creates room for healthcare facilities to share necessary information (Hernandez-Boussard, Blayney & Brooks, 2020).  Through the EHR, it easy to develop the building blocks desirable to recognize quality metric information in EHRs. It is of the essence to create an EHR database, map quality metrics to medical vocabularies, and develop electronic quality metric phenotypes. The EHR program creates a web-based tool that integrates the empirical evidence and clinical characteristics to evaluate patient personalized risk prediction that assists care providers and patients in selecting a treatment option (Hernandez-Boussard, Blayney & Brooks, 2020). These options provide the best-anticipated quality of care given the significance they assign to each patient-centered outcome. Making use of EHR will assist in addressing a crucial gap in evidence for prostate cancer therapy and research by offering care providers and patients practical evidence desirable to contrast the trade-offs among patient-centered outcomes across various treatments.

Prostate cancer is a complex illness, and existing therapies have associated risks of patient-centered outcomes, although no definite evidence exists on the variation of hazards across treatments (Hernandez-Boussard, Blayney & Brooks, 2020). Therefore, this article’s proposal develops measures to gather patient-centered outcomes recorded in EHRs and a risk evaluation tool to estimate the personalized hazards of issues across therapies. Such data will assist policymakers and healthcare staff in enhancing a patient’s healthcare experience and results (Hernandez-Boussard, Blayney & Brooks, 2020). Moreover, Electronic Health Records helps clinicians by making it easy to gather and analyze information regarding patients in a meaning full manner that keeps track of patients over time and recognize trends associated with cancer. According to studies concerning cancer diagnosis and therapy, using EHRs is associated with enormously higher healthcare quality for cancer (all types of cancer) (Hernandez-Boussard, Blayney & Brooks, 2020). The EHRs are essential since it provides information without difficulty keeps records of treatment and enables individuals to assess knowledge better.

In the current healthcare organization, no one is engaged in any unit of healthcare delivery, or planning can fail to recognize immense changes in the perspective of data management (Magyar, 2017). This article reviews examples of healthcare databases used in healthcare organizations to enhance patient care concerning cancer therapy (Magyar, 2017). For one to comprehend the range of EHRs that the healthcare department might access and why there might be a concern in regards to the protection of individual data, care providers are obliged to consider various factors of EHRs data management such as comprehensiveness (Magyar, 2017). According to healthcare studies, comprehensiveness portrays the completeness of data of patient’s healthcare experiences and data pertinent to an individual patient (Magyar, 2017). Comprehensiveness incorporates the amount of data care providers have regarding patients both for each personal experience with the healthcare department as well as treatment procedures. Data that is comprehensive incorporates demographic information, organizational data, health risks, and conditions, patient therapeutic history, existing management of health status, and result statistics (Magyar, 2017).

• Demographic information

Demographic information consists of statistics including age, race, gender, national origin, marital standing, address of dwelling, names of direct relatives and other details concerning direct relatives, and alternative data (Magyar, 2017). Moreover, demographic data in the EHR also include information regarding employment status and employers, education level, and some indicator of socioeconomic rank.

• Administrative Information

Administrative data incorporates information concerning health insurance such as membership and admissibility, dual coverage, and obligatory copayments and deductibles for a provided benefits bundle (Fox, Aggarwal, Whelton & Johnson, 2018). Administrative data commonly recognize care providers with an exclusive identifier and probably offer extra specific data. These may comprise the kind of physician, physician specialty, and culture of the institution.

• Health risks and Health status data

Health risks data reflects the lifestyle and characters of an individual. For instance, in cases of cancer patients, the care provider might ask if the individual uses tobacco products or regularly participates in strenuous activities (Magyar, 2017). Health risk data also includes information about genetic factors and family history, such as whether a person has first-degree relatives with a significant class of cancer.

Health status information is generally and often reported by individuals themselves. Health condition data reflects factors of health such as physical status, emotional and mental actuality, intellectual functioning, communal and role functioning, and observations of an individual’s health in the past, current, and future and contrasted with that of an individual’s peers. Health conditions and quality of life measures are commonly considered outcomes of healthcare (Fox et al., 2018). Still, evaluators and researchers also require such information to record their analysis of the mix of patients and the range of severity of health status.

• Patient Therapeutic History

Patient medicinal history incorporates information on previous health check encounters, including hospital admissions, surgical processes, pregnancies, and live births (Fox et al., 2018). It contains data on past medical issues and probably the family history of events such as intoxication or parental separation (Magyar, 2017). Additionally, although such information is essential for quality care, they may be vital for case-mix and severity alteration.

• Existing Medical Management

Existing medical management includes information in regards to the gratification of experience procedures and parts of the patient file (Magyar, 2017). Such data might replicate health screening, existing health issues, and diagnosis, treatment processes conducted, laboratory tests performed, and counseling offered.

• Outcomes information

Outcomes data includes a range of choices of procedures of the effects of health care and the outcome of different health issues across the spectrum, from mortality to increased stages of performance and wellbeing (Fox et al., 2018).  Outcomes data reflect healthcare occasions such as readmission to healthcare institutions or unplanned difficulties and side effects of care. Consequently, outcome data often incorporates measures of satisfaction with patient care.  Results evaluated weeks or months after therapy procedures, and by information straight from individuals or immediate relatives, are desirable. However, such data appear to be the least commonly found in the secondary record (Magyar,2017). The EHRs program manages and presents the historical and existing test result in suitable healthcare providers. Through this, healthcare professions can review the patient’s information with the ability to filter and compare the outcome. Additionally, the EHR system allows physicians to manage patient records electronically and store them for future references.

Conclusion

According to medical studies, the more inclusive the EHR is, the more present and probably more sensitive data regarding patients is likely to be. The comprehensiveness of the Electronic Health Records has a significant correlation with concerns regarding confidentiality and privacy. One of the most significant approaches to ensure individuals have complete advantage of the benefits of EHRs and enhance quality care, preventive cancer care, and patient outcome is to attain meaningful use. By healthcare institutions achieving meaningful use, they can obtain benefits beyond monetary enticements. Over the past years, approximately every significant healthcare institute invested majorly in computerization. These technological advances, such as EHRs, are allowing care providers to present a faster and more efficient patient outcome.

References

Magyar, G. (2017). Blockchain: Solving the privacy and research availability tradeoff for EHR data: A new disruptive technology in health data management. In 2017 IEEE 30th Neumann Colloquium (NC) (pp. 000135-000140). IEEE.

Hernandez-Boussard, T., Blayney, D. W., & Brooks, J. D. (2020). Leveraging digital data to inform and improve quality cancer care.

Fox, F., Aggarwal, V. R., Whelton, H., & Johnson, O. (2018, June). A data quality framework for process mining of electronic health record data. In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 12-21). IEEE.

DNP 805 Assignment EHR Database and Data Management Essay

Patient Clinical Problem

Adult patients undergoing colon cancer surgery who are prone to surgical site infections are the clinical issue of focus. Individuals undergoing colorectal cancer surgery had an elevated risk of morbidity (20%-40%) and mortality (2%), mostly due to postoperative surgical site infections, according to Li et al. (2018). According to reports, the risk of postoperative infections in patients undergoing colon surgery has grown by more than 25%. (Grundmeier et al., 2019; PSNet, 2019). Infections, particularly hospital acquired infections (HAIs), increase the length of stay in health facilities, increase susceptibility to other illnesses, and raise medical costs. Providers and organizations can use creative ways, such as the creation of EHR databases, to manage data to prevent and minimize the occurrence of surgical site infections.

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.

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:

1. Select a clinically based patient problem in which using a database management approach provides clear benefit potential.
2. 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).

Structured and Unstructured Data

According  to Dey, et al., (2016), “Copious longitudinal structured and unstructured data are captured by EHRs to characterize the patient’s demographic (e.g., age, sex, address), health and treatment status, diagnoses, lab test results, and medication orders”. They go on to explain that, “As much as 80% of the EHR data is thought to be in unstructured form and to effectively use EHR data it is important to understand how the data comes to be” (Dey, et al., 2016).

Structured data are objective data or hard data that are populated in the EHR, such as vital signs, laboratory values, patient demographics, dates, names, diagnosis codes, identification numbers, and “specific words and short phrases that are often presented in an easy-to-use user point-and-click interface via drop-down boxes with options to select the item of interest” (GCU, 2014). So therefore  in predicting sepsis these data play a vital role, especially the vital signs and laboratory results.

On the other hand, unstructured data, are data points “that are typically “free text” in progress notes and care plans, comments embedded in the flow sheet, and the like”(GCU, 2014). These comments and notes often contain valuable information that expands upon the structured data and that can provide beneficial input for a database designed to map to patient care, but unfortunately, unstructured data are not easily captured in the somewhat inelastic programming processes of a computer system. That is where innovative and user-based database design comes into play” (GCU, 2014). “Unstructured data is the information that typically requires a human touch to read, capture and interpret properly. It includes machine-written and handwritten information on unstructured paper forms, audio voice dictations, email messages and attachments, and typed transcriptions–to name a few” (DataMark, 2013)

1. 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.
2. 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?

3. 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.

Database is a collection of a specific and well arranged data that are entered and stored in a server. Healthcare information technology (HIT) is an area in which the concern is to have a full grasp of understating on how healthcare database concepts and formats work. There are elements that are important in database such as text and notes.

For example, in Labor and Delivery department wherein elements are entered in what it’s called pre-natal questionnaire. These elements comprise the needed information gathered from the patient who is currently pregnant and / or expected to deliver anytime. All the data includes the date, time, numbers, and text that are all indicated, completed, answered, and written in the pre-natal questionnaire by the patient seen outside the hospital including the clinic (private or non-private) and community health center (CHC). An example of population is in this department is the pregnant migrant workers.

Aside from the text and notes included as elements of database, numbers are also important for this population. Under text and notes, includes 1) expected date of delivery (EDD) with demographics – age, date of birth (DOB), race, marital status, occupation, address, education, language (English or non-English), insurance carrier, husband/domestic partner, emergency contact, phone number  2) gynecology (GYN) history – age of menarche, duration of menstrual flow, abnormal pap smear, age of first child, monthly menses, frequency of cycle, birth control at conception (BCP), HVP vaccine, sexually transmitted disease (STD)  3) past medical history- such as mental disorders, hypertension, diabetes 4) obstetrics history – EDD calculation/update (fundal height, ultrasound date, final EDD), number of pregnancies/abortions

All the indicated information is answered in the form of date, time, numbers, and text in this pre-natal questionnaire. Each of these elements are significant in the process of caring for such population either during the first, second, or third trimester. For instance, if there is a problem or abnormality with the data entered any moment during the pregnancy, then the attending physician will intervene immediately to resolve the concern.

Among the listed elements which I think are all valuable in a database, I would like to give emphasis with demographics particularly the education and language and past medical history like mental disorder. If there is a knowledge deficit noted even from the beginning of the pregnancy, the attending physician and L and D nurse need to know about it. Healthcare staff need to make sure that all the patient education given during the entire process of pregnancy is fully understood by the patient. Safety of both patients (mother and newborn) will be the priority. As such, having a patient’s plan of education with the husband / domestic partner is important in every stage of the pregnancy from conception to time of delivery.  And that support will be provided by the attending physician and other healthcare staff once the need of it is identified.

Having a language barrier is another challenge when disseminating patient education. And every time the patient comes to the clinic for consultation and follow up, all the concerns are addressed accordingly, and that miscommunication will be prevented. If there is a need to get a translator in order to make sure that everything is clear to the patient will be better than assuming that the patient understands it. Perhaps the healthcare staff can do “teach back,” if needed. Another element that is valuable in the database is the past medical history such as mental disorder. Pregnancy can bring a lot of changes to the patient, not only physically but emotionally as well. Ensuring that the patient is mentally fit to handle a big change in herself is crucial during the entire pregnancy.  According to Fellmeth, G., Fazel, M., & Plugge, E. (2017), pregnant migrant women are at in danger of perinatal mental disorders related to various stressors encountered before, during, and after migration.

Each of these elements is part of a bigger puzzle that the attending physician and Labor and Delivery nurse need to know and be familiar with in order to fully care for such population. These elements that are valuable in the database 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). Every known element in the database in this pre-natal questionnaire will surely indicated the progress of the pregnancy.

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

Fellmeth, G., Fazel, M., & Plugge, E. (2017). Migration and perinatal mental health in women from low- and middle-income countries: a systematic review and meta-analysis. BJOG : an international journal of obstetrics and gynaecology, 124(5), 742–752. https://doi.org/10.1111/1471-0528.14184

Attachments

DNP-805A-RS-DatabaseConceptMap.do

EHR Database and Data Management – Rubric

Collapse All EHR Database And Data Management – RubricCollapse All

Selection of Clinically-Based Patient Problem

10 points

Criteria Description

Selection of Clinically-Based Patient Problem

5. Excellent

10 points

Patient problem selection indicates clear benefit potential by using a database management approach. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

4. Good

9.2 points

Patient problem selection indicates clear benefit potential by using a database management approach. Discussion is convincing. Information presented is from scholarly though dated sources.

3. Satisfactory

8.8 points

Patient problem selection indicates clear benefit potential by using a database management approach, but the connection is perfunctory.

2. Less Than Satisfactory

8 points

Patient problem selection indicates some benefit potential by using a database management approach, but the connection is marginal or incomplete.

1. Unsatisfactory

0 points

Patient problem selection does not provide clear benefit potential by using a database management approach.

Identification of Data Needed to Manage the Patient Problem Using Information From the Electronic He

10 points

Criteria Description

Identification of Data Needed to Manage the Patient Problem Using Information From the Electronic Health Record (EHR)

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5. Excellent

10 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is present in full. Discussion is convincing and defines specific elements. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: DNP 805 Assignment EHR Database and Data Management Essay

4. Good

9.2 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is present in full. Discussion is convincing and defines specific elements. Information presented is from scholarly though dated sources.

3. Satisfactory

8.8 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is present but at a perfunctory level.

2. Less Than Satisfactory

8 points

Identification of data needed to manage the pati