The Application of Data to Problem-Solving
In the modern era, there are few professions that do not to some extent rely on data. Stockbrokers rely on market data to advise clients on financial matters. Meteorologists rely on weather data to forecast weather conditions, while realtors rely on data to advise on the purchase and sale of property. In these and other cases, data not only helps solve problems, but adds to the practitioner’s and the discipline’s body of knowledge.
Of course, the nursing profession also relies heavily on data. The field of nursing informatics aims to make sure nurses have access to the appropriate date to solve healthcare problems, make decisions in the interest of patients, and add to knowledge.
In this Discussion, you will consider a scenario that would benefit from access to data and how such access could facilitate both problem-solving and knowledge formation.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
WEEKLY RESOURCES
To Prepare:
- Reflect on the concepts of informatics and knowledge work as presented in the Resources.
- Consider a hypothetical scenario based on your own healthcare practice or organization that would require or benefit from the access/collection and application of data. Your scenario may involve a patient, staff, or management problem or gap.
By Day 3 of Week 1
Post a description of the focus of your scenario. Describe the data that could be used and how the data might be collected and accessed. What knowledge might be derived from that data? How would a nurse leader use clinical reasoning and judgment in the formation of knowledge from this experience?
By Day 6 of Week 1
Respond to at least two of your colleagues* on two different days, asking questions to help clarify the scenario and application of data, or offering additional/alternative ideas for the application of nursing informatics principles.
*Note: Throughout this program, your fellow students are referred to as colleagues.
Solution
Improving Post-Surgery Recovery for Cardiac Patients
In my healthcare practice, I work in a cardiac care unit where patients who have undergone major cardiac surgeries, such as coronary artery bypass grafting (CABG) or valve replacement, are monitored and treated. One common issue we encounter is the variability in recovery times and complications among patients post-surgery. Some patients recover quickly and without incident, while others experience delayed healing, infection, or other complications, despite having similar surgery types. This disparity in recovery outcomes is a challenge both for patient care and for managing healthcare resources efficiently.
Data that Could Be Used:
Patient Demographic Data: Age, gender, medical history (e.g., diabetes, hypertension, obesity), and previous heart conditions.
- Surgical Data: Type of surgery performed, duration of surgery, any complications during the procedure, and anesthesia used.
- Post-Surgery Recovery Metrics: Vital signs (blood pressure, heart rate, temperature), wound healing progress, mobility (physical therapy progress), pain levels, medication usage (e.g., pain management, antibiotics), and lab results (e.g., inflammation markers).
- Patient Monitoring Data: Real-time data collected through smart devices or remote monitoring tools, such as heart rate variability, oxygen levels, and activity levels in the post-surgery period.
- Hospital Utilization Data: Length of stay, readmission rates, and the need for follow-up interventions.
- Nurse and Healthcare Staff Feedback: Qualitative data from nursing staff and other clinicians involved in the patient’s care, including observations of patient engagement, communication, and any barriers to recovery.
How the Data Might Be Collected and Accessed:
- Electronic Health Records (EHR): This will provide access to demographic, surgical, and recovery data.
- Patient Monitoring Devices: Devices like heart rate monitors, pulse oximeters, and wearable technology can continuously collect real-time data about the patient’s recovery progress.
- Patient Surveys/Questionnaires: Direct feedback from patients regarding their pain levels, mobility, and overall health can be gathered through digital or paper-based surveys.
- Clinical Databases: Data on length of stay, complications, and readmissions can be accessed from hospital databases or quality monitoring systems.
- Health Information Exchange Systems: If available, these systems could allow for data sharing across institutions to provide a broader context (e.g., if patients have been transferred between facilities).
Knowledge Derived from the Data:
By collecting and analyzing these data points, we could uncover valuable insights into the factors that contribute to slower recovery times or post-surgical complications. For example, we might find that patients with certain comorbidities (such as diabetes or obesity) or specific surgical characteristics (e.g., longer surgery duration or intraoperative complications) tend to experience longer recovery times or higher complication rates.
Finally, we could identify any gaps in patient education or communication that might be hindering recovery, such as unclear instructions on wound care or rehabilitation exercises.
Clinical Reasoning and Judgment in the Formation of Knowledge:
A nurse leader would play a pivotal role in interpreting this data. Using clinical reasoning, the nurse leader would identify relevant patterns or correlations that could lead to improvements in care. For example, they might notice a trend where patients with a certain age group or comorbidity profile experience delayed recovery, suggesting that a more personalized or tailored post-surgical plan might be necessary for these patients.
References
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American Nurses Association (ANA). (2015). Nursing: Scope and standards of practice (3rd ed.). American Nurses Association.
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Bakken, S., & Dinev, T. (2011). Nursing informatics and health information technology: The way forward. Nursing Outlook, 59(2), 86–92.
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Cresswell, K., & Sheikh, A. (2013). The NHS care record service: A case study of the application of information technology in the English NHS. Journal of the Royal Society of Medicine, 106(12), 473–476.
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HIMSS Analytics. (2015). The value of health data: How clinical data drives better care. Healthcare Information and Management Systems Society (HIMSS).
