DNP 805 Week 3 Using CPOE and CDSS

Grand Canyon University DNP 805 Week 3 Using CPOE and CDSS – Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University DNP 805 Week 3 Using CPOE and CDSS   assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.

How to Research and Prepare for DNP 805 Week 3 Using CPOE and CDSS      

Whether one passes or fails an academic assignment such as the Grand Canyon University NUR 550 Benchmark – Evidence-Based Practice Project: Literature Review depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.

After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.

How to Write the Introduction for DNP 805 Week 3 Using CPOE and CDSS      

The introduction for the Grand Canyon University DNP 805 Week 3 Using CPOE and CDSS   is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.

How to Write the Body for DNP 805 Week 3 Using CPOE and CDSS      

After the introduction, move into the main part of the DNP 805 Week 3 Using CPOE and CDSS  assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.

Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.

How to Write the Conclusion for DNP 805 Week 3 Using CPOE and CDSS      

After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.

How to Format the References List for DNP 805 Week 3 Using CPOE and CDSS      

The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.

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Sample Answer for DNP 805 Week 3 Using CPOE and CDSS

Using CPOE and CDSS 

Over the years, healthcare professionals have dealt with various conditions and illnesses that trouble individuals and populations. While some of these illnesses onset away from the hospital and thus require hospital admissions, some are developed while individuals have been admitted into the hospitals. One such condition is pressure ulcers. Even though various strategies have been applied to prevent and manage pressure ulcers, the condition is still prevalent and impacts patients negatively (BoykoTatiana et al., 2018). In particular, the pain resulting from the wounds usually causes patients suffering, calling for better prevention and management of the same. In attempts to find better remedies, various stakeholders have turned to technological solutions. For example, the use of Clinical Decision Support System (CDSS) and the Computer Provider Order Entry (CPOE) have particularly been used in supporting the medication administration for better pharmacological outcomes (Shahmoradi et al., 2021). Therefore, the purpose of this assignment is to formulate a CDSS using a CPOE to be integrated into the Electronic Health Records to support the management of pain among patients experiencing pressure ulcer 

The CDSS Design and the Rationale Behind the Design 

The Computer Provider Order Entry entails using a computer for medication order entries as well as storage of the orders digitally. The CPOE also enables the entry of other data such as a patient’s imaging data, diagnostic test results, laboratory test results, and discussions held between the professionals. Traditionally, CDSS was majorly characterized by helping professionals make appropriate care and patient decisions. However, the current systems even allow for a display of an individual’s past status while supporting other evaluations and recommendations (Shahmoradi et al., 2021). As such, the advantages of using CDSS can be tapped and fine-tuned to ensure that patients with pressure ulcers get better pain management hence improved outcomes. 

The designed CDSS is to be composed of a computer algorithm created to gather the patient’s important information such as the existing medication, comorbidities, the patient’s skin condition, the degree of pain, age, and gender. As part of the system, the system will be key in offering the clinicians the necessary support when deciding on the best medication to be given to the patient depending on the collected data. The core design of the system will also have definitions, queries, and access tables which help in matching an individual’s current health condition with what is in the electronic health record. This system has been proposed to help eliminate possible medication errors and prevent possible adverse drug interactions when offering care to patients with pressure ulcers (Shahmoradi et al., 2021). The elimination of medication errors and avoidance of drug interactions is obtained by giving the information in a simple and easily understood form and format. The proposed system will also be formulated so that it can easily be integrated into the facility’s electronic health record system. 

The Implementation of the System and its Adoption by Fellow Clinicians. 

A good plan should be in place to enable a successful implementation of the designed system. As part of the plan, the staff will have to undergo a comprehensive education and training session. The content of the training will be tips on using the new system, the reasons why the facility needs the new system, the main features and the advantages of using it. As such, a total period of two weeks will be set aside for training and testing the new system. The staff workflows and responsibilities are to be modified to ensure that every staff engaged in the management and care of the patients with pressure ulcers can easily log in to the system and check the status of any patient of concern. Security is important when using such systems; therefore, anyone logging into the system will be required to undergo a one-step password authentication to only allow the right users.  

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After logging in, the clinician then in feeds the current information, such as the pressure ulcer indicators, into the EHR, opens the CDSS, and reviews the treatment options available for the patient as information to the individual’s health information. The clinician then makes an assisted decision on the best medication that can be administered to the patient. Such a decision is then followed by the use of a CPOE to make medication order. The clinician then reviews the medication information that appears on the resolution table to ensure that everything is aligned. If there are any adjustments to be carried out, the professional states such a reason through a drop-down selection. 

Potential Challenges and Possible Solutions. 

The use of the created CPOE-assisted CDSS system in the facility implies that the organization’s operations will have to undergo some changes and adjustments. Evidence has it that introducing a change process in any organization can come with various challenges. One of the prime challenges is staff resistance to the change being introduced. Such resistance may stem from various reasons, including viewing the proposed change as bothersome, existing misconceptions regarding CPOE and CDSS, and possible fear of the unknown (Westerbeek et al., 2021). Solving such a resistance to the proposed change is key as resistance is known to negatively impact the implementation process, which can also extend to the use of the implemented system. 

Offering a comprehensive education and training on the new system to the staff is one key strategy that can be used in solving resistance. In such a training, the staff is given valid reasons why the facility needs the new CDSS system and what expected benefits would be experienced upon its implementation. The new system will be integrated into the electronic health system, which has been key in simplifying the work to help in enhancing patient care and outcomes too. The problem of medication errors is not a secret in any patient care setting, and therefore, such incidences can be used to ensure that the staff buys into the idea of using the CDSS system to lower the incidence of medication errors. When the new system is used, there are high chances that the medication error incidences will greatly be reduced (Westerbeek et al., 2021). The staff can also be trained on how to operate the new system to help do away with resistance caused by insufficient knowledge of how to operate the new system. Again, the staff also needs to be trained and informed on the integration of the new system into the already existing EHR system. 

The other barrier is the fear of potential loss of autonomy (Westerbeek et al., 2021). The staff may resist the use of the new system due to the fear that the new system would snatch away their autonomy as the system does the work more accurately and promptly. The implication is that with the new system in place, the best treatment regimen for the patients can be offered even without the clinician’s input. This barrier can be overcome by assuring the staff that the new CDSS system will only assist in reducing their workload. Hence they can put more energy into special areas of patient care to improve care outcomes. 

Conclusion 

The demand for better and more efficient patient care and services in the care setting has led to various inventions and innovations. CDSS and CPOE have been used for years to improve patient outcomes. However, a combination of these two technologies still presents genuine opportunities to exploit and ensure that patient outcome are better. Therefore, a  CPOE-assisted CDSS system has been proposed to improve the use of pain medications to enhance the management of pain among patients with pressure ulcers. This system can be embedded into the electronic health record system to improve care outcomes. 

References 

BoykoTatiana, V., LongakerMichael, T., & YangGeorge, P. (2018). Review of the current management of pressure ulcers. Advances in Wound Care. https://doi.org/10.1089/wound.2016.0697 

Shahmoradi, L., Safdari, R., Ahmadi, H., & Zahmatkeshan, M. (2021). Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects. Medical Journal of the Islamic Republic of Iran, 35, 27. https://dx.doi.org/10.47176%2Fmjiri.35.27. 

Westerbeek, L., Ploegmakers, K. J., de Bruijn, G. J., Linn, A. J., van Weert, J. C., Daams, J. G., … & Medlock, S. (2021). Barriers and Facilitators Influencing Medication-Related CDSS Acceptance According to Clinicians: A Systematic Review. International Journal of Medical Informatics, 104506. https://doi.org/10.1016/j.ijmedinf.2021.104506 

DNP 805 EHR Database and Data Management Sample

EHR Database and Data Management 

Databases in the medical field provide a suitable framework for collecting, analyzing, and monitoring vital health information such as tests, expenditures, invoicing and transactions, patient information, etc. These records must be stored private from the general public while being widely available to health care providers who utilize them to save lives (Pastorino et al., 2019). This paper seeks to describe how a database can be used to diagnose chronic obstructive pulmonary disease (COPD) early diagnosis. 

Clinically Based Patient Problem 

COPD is a prevalent long-term condition marked by acute respiratory cough and shortness of breath, coughing, and sputum secretion. COPD is typically caused by prolonged exposure to hazardous chemicals and pollutants (Agarwal et al., 2022). Smoking accounts for approximately 85 percent of patients with COPD (Asamoah-Boaheng et al., 2022). Smoking is the leading cause of respiratory injury and asthma. COPD may also be caused by smoke inhalation from fuel combustion (Choi & Rhee, 2020). If the evidence is in the person’s private files, the caregiver may ignore it. When the problem exacerbates, non-smokers are usually diagnosed with COPD (Choi & Rhee, 2020). Exacerbation is defined by deteriorating health problems such as increasing dyspnea, continuous sneezing, and a change in the color of the sputum (Holmes & Murdoch, 2017). Exacerbations individuals incur higher healthcare expenditures, and certain drugs used in therapy, such as corticosteroids, have long-term negative consequences (Asamoah-Boaheng et al., 2022). COPD might also be caused by genetic anomalies, including severe hereditary impairment of alpha-1 antitrypsin (AATD) (Asamoah-Boaheng et al., 2022), which could be overlooked in large amounts of data. 

Individuals have one of two phenotypes that vary in intensity: acute emphysema or bronchiolitis. COPD must be evaluated in individuals who have difficulty breathing, sputum secretion, or persistent cough (Agarwal et al., 2022). Nevertheless, there are various other diagnoses for COPD, such as anaemia, lung cancer, persistent asthma, etc. COPD is often associated with concurrent chronic conditions such as diabetes and obesity, both of which produce various COPD-related symptoms such as cough and shortness of breath (Choi & Rhee, 2020). This postpones the diagnosis of COPD. The best technique to verify COPD in a person is high-quality spirometry. Spirometry is recommended when COPD is detected, and for a non-smoker with associated conditions, spirometry may be performed when the disease has progressed. The slow symptom onset further distinguishes COPD, so an individual may fail to detect dyspnoea despite having chronic coughing, causing COPD diagnosis to be delayed. 

Early diagnosis is important in establishing the appropriate treatment course considering the individual’s severity and phenotype. Early detection has been found to enhance treatment experience by lowering the incidence and frequency of flare-ups, lowering treatment costs, and preventing long-term adverse effects associated with pharmacological treatment (Asamoah-Boaheng et al., 2022). Hospitals also avoid wasting money owing to erroneous treatment (Choi & Rhee, 2020). 

Conceptual Database Design 

The healthcare database is the backbone of the electronic health record, holding a wealth of organized and unstructured user data (Pastorino et al., 2019). The database material will only be relevant in the earlier detection and successful management of COPD if the unorganized data is analyzed to provide data that can be put inside the predefined metadata. The intended patient result in building the healthcare database will be the early identification of COPD by giving the provider access to patient health information from the database, which will then be utilized to detect COPD. The database shall contain comprehensive and accurate information received from or entered at the various hospital settings where the person has been treated. The data must also be accurately evaluated and structured, making it easier to remove differential diagnoses and do COPD confirmation spirometry. 

The data items needed to construct the database to facilitate early diagnosis of COPD are identified and categorized as unstructured or structured during the conceptual design stage. Whereas structured data from the EHR may be directly filled into the system, unstructured data from caregivers’ and doctors’ notes are processed using natural language technologies to make data sensible within the environment. The relational database will include a healthcare-specific guideline and a language processing technology. The natural language-specific guideline deconstructs unstructured format input to enable NLP creators to execute specialized natural language processing inspections, including detecting the presence of vague terms. The healthcare-specific benchmark will then seek the relevant healthcare words and record the proper information in the correct fields depending on the context of the NPL transcribers. 

Thus far, the database executes conventional database operations. The registry will include a unique COPD risk area to aid early detection. Whenever health records are processed, all past respiratory illnesses will be recorded under the COPD risk area. This section will be filled with indicators such as a persistent cough, breathlessness, and sputum secretion. The space will be filled with debris and hazardous gases from the patient’s surroundings. COPD is distinguished by the slow onset of symptoms that may or may not occur concurrently. Initially, an individual may describe dyspnoea without other symptoms, which may be missed and linked to a concurrent condition. Nevertheless, the data will be saved in the database so that when the person experiences a new symptom, such as mucus production, and the nurse brings up their record, the COPD risk area will be updated with all the identified symptoms thus far leading to early diagnosis. If the facility has a CDSS connected to the database, the CDSS program will notify the physician of a possible COPD diagnosis. 

Attributes and Data Entities 

1. Patient Details

This entity is a personal identification; it is connected to all the other properties which provide this person’s profile in the medical setting. All of its characteristics are organized, and these traits are objective. 

Attributes 

• Name (Varchar) 

• Gender (Boolean) 

• Age (Int) 

• Address (Varchar) 

2. Health Status

The entity represents the patient’s medical history as evaluated by the physician. Because the physician’s assessment and perception of the consequences of the patient’s condition may differ, all of the qualities are unstructured. 

Attributes 

• BMI (Int) 

• Height (Int) 

• Weight (Int) 

• Blood Pressure (Int) 

Because data submitted into the database is not primarily utilized for the initial COPD diagnosis, the entities described below will include more than the given characteristics. On the other hand, the features indicated must be included in the COPD risk section if the risk exists. 

4. 4. Genetics

The entity has qualities that describe physical and genetic anomalies that may impact the likelihood of COPD. 

Attributes 

• Alpha-1 Antitrypsin Dysfunction (Boolean) 

3. 3. Environment

Identifies the patient’s regular surroundings, which may raise COPD risk. 

• Second-hand smoking inhalation (Boolean) 

• Use of biofuels (Boolean) 

• Work-related toxic contact (Boolean) 

6. 6. Medical history 

Specifies the health history and updates the COPD risk field with factors that elevate the chance of COPD. 

• Smoking history (Varchar) 

• Smoking Frequency (Varchar) 

• Respiratory diseases (Varchar) 

5. 5. Signs and Symptoms

This entity explains the patient’s present symptoms and the rationale for the hospitalization. Every symptom is regarded as a separate feature. The following symptoms will be included in the COPD risk field:  

• Severe cough 

• Sputum secretion 

• Dyspnoea 

The logical data model architecture concerns data modeling, which specifies the link between the objects. Figure 1 below illustrates the general conceptual map for the proposed database. 

Figure 1: Conceptual map 

Conclusion 

The significance of a database in medical diagnosis cannot be ignored. It is critical for physicians, caregivers, and executive management to have timely and error-free access to detailed patient data. Hospital services rely on the efficiency, accuracy, and effectiveness of medical databases to ensure timely diagnosis and treatment, as in the case of COPD. 

References 

Agarwal, A. K., Raja, A., & Brown, B. D. (2022). Chronic Obstructive Pulmonary Disease. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK559281/ 

Asamoah-Boaheng, M., Farrell, J., Osei Bonsu, K., & Midodzi, W. K. (2022). Examining risk factors accelerating time-to-chronic obstructive pulmonary disease (Copd) diagnosis among asthma patients. COPD: Journal of Chronic Obstructive Pulmonary Disease, 19(1), 47–56. https://doi.org/10.1080/15412555.2021.2024159 

Choi, J. Y., & Rhee, C. K. (2020). Diagnosis and treatment of early chronic obstructive lung disease(Copd). Journal of Clinical Medicine, 9(11), 3426. https://doi.org/10.3390/jcm9113426 

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: An overview of the European initiatives. European Journal of Public Health, 29(Supplement_3), 23–27. https://doi.org/10.1093/eurpub/ckz168