Despite the past few years of challenges in the nation’s healthcare systems, future outcomes for patients and providers depend on several factors. Still, as we reflect on those burdens and the current state of healthcare, we know that technology and its advancements will likely be the most vital.
Digital Tech Will Create Transformative Healthcare
Embracing the Future of Nursing
The trials that began in early 2020 put healthcare systems into a whirlpool with supply, staff, and hospital bed shortages. The increase in patients (in addition to those in the rapidly aging population) and health risks also expanded government directives, regulations, and care costs. So much strain inevitably contributed to the decline in nurses as many left or were discouraged from pursuing the field, leading to an even greater number of adverse patient outcomes.
However, even before the global pandemic, surveyed next-gen nurses reported that interdisciplinary healthcare was the key to treating and caring for patients based on ongoing evidence. Newer nurses were shown to be willing to step outside the box and buck tradition if data indicated that a new method or piece of technology could benefit patients. Care consistency, new care models, and point-of-care decision support were crucial to best meeting patient needs.
Although preventative care and interprofessional teams are still seen as central components of care, technology has become more prevalent post-pandemic when so many clinicians have left their field. More must be done with fewer resources. Tech-savvy nurses are increasingly the norm in healthcare settings.
“In 2023, we expect to see more widespread use of smart technology and big data analytics in healthcare. This includes monitoring vitals, reviewing electronic patient health records, and automating simple medical tasks. As a result, nurses can offer richer, more personalized care to their patients.”1
Technology’s Role in Patient Treatment, Safety, and Comfort
Information becomes more readily available as the clinician’s relationship with technology changes. This aspect will foster a more comfortable relationship between clinicians and their patients.
Thorough knowledge of the patient enhances the nursing care process (NCP), which consists of assessment, diagnosis, planning, implementation, and evaluation. Personal profiling, which exists in endless facets of technology to date, lends itself to big data for a better NPC and can be tailored to all areas of patient treatment. This “precision medicine” helps to customize individual care plans by categorizing patient biological and demographic data; personal and family medical history; and immune, environment, and metabolic information to complete their profile and allow complex AI algorithms using their EHR (electronic health records) to narrow and streamline treatment options via producing metadata.2
While patient needs constantly change, nurses must be able to thoroughly input and access all data provided. Technology can now assist with this task in more comprehensive ways data. Per the Healthcare Information and Management Systems Society (HIMSS, the Technological Competency as Caring in Nursing (TCCN; Locsin, 2016) theory first arose to show that the entire understanding of patient health can be achieved via digital methods and help to predict adverse events like falls:
Computerization of nursing documentation, medication bar code scanning, and diagnostics results integration help create clinical decision support (CDS) systems that guide nurses in providing the best care pathways for patients. Several studies look at the use of AI in predicting patients’ risk of falling and how the risk scores influence the CDS deployed by nurses. Compared to manually inputted fall risk screening tools, the predictive analytics using AI outperformed the manually inputted tool in the accuracy and timing of fall risk assessment based on the studies of Lytle et al. (2021), Cho et al. (2019), Moskowitz et al. (2020), Yokota et al. (2017) and Jung et al. (2019).
Electronic health records (EHR) produce metadata used for research and advanced clinical practice. Lytle et al. (2021) used twenty-seven million patient encounters to develop the content format and standard documentation workflow for the fall prevention information model. Using FloMap software, EHR data were mapped and grouped into concepts that form the information model (IM). The fall prevention IM includes nursing assessment, intervention, and outcomes. The concept of IM allowed data comparison across other organizations. It provided a better understanding of how nursing documentation can best be utilized. An earlier study by Cho et al. (2019) utilized nursing records to create a predictive model to assess a patient’s falling risk, showing an error rate of 11.7% and c-statistics of 0.96, which showed superior performance compared to manually inputted fall risk screening tools.
Cho et al. performed a follow-up study in 2021 to review the impact of an electronic analytic tool for predicting falls and the nurses’ response to the patient’s risk. The authors used a control group and an intervention group to compare the effects of a project called Intelligent Nursing @ Safety Improvement Guide of Health Technology Systems (IN@SIGHTS). There was a decrease in the mean fall rate in the intervention from 1.92 to 1.72, which revealed that the analytic tool could lower the number of falls and lead to positive changes in nursing interventions (Cho et al., 2021).
Pharmacology side effects proved to be a fall risk contributor coined as Fall-Risk Inducing Drugs or FRIDs (Choi et al., 2018). A dynamic EHR-based fall risk prediction model for hospitalized patients given FRIDs using machine learning and AI showed an unbiased c-statistics of 0.62 compared to the Morse Fall Score of 0.69. Each FRIDs administered increases the patients’ odds of falling by 8% (Choi et al., 2018). Current fall risk screening tools commonly used in healthcare facilities do not consider most drugs in the FRIDs category. The Fall Predictive Analytics Tool, a program in the Epic EHR, can cognitively compute the number of FRIDs doses and medications administered at a given point in time and assign a patient’s fall risk status along with other variables the nurses would typically not have the time to review when performing a patients’ typical fall risk assessment.
Risk evaluation and re-evaluation are crucial as patients’ conditions change (Yokota et al., 2017). A discriminant model called Find Fall Risk of Inpatients from Nursing Data (FiND) was created to evaluate the patient’s fall risk using the previous day’s nursing documents.
The use of technology in predicting patient risk for falling is promising. Embedded information from clinical documentation personalized clinician decision support that could assist in an individualized approach for a fall prevention strategy. Implementing the Epic Cognitive Computing Fall Risk Model can capture variables embedded in the EHR to enhance the predictability of a patient’s fall risk and potentially decrease nurses’ documentation time (Epic, 2020).vii
The Role of Virtual Observation
As fall risk predictors continue to become available, virtual observation technology has established itself as a tool for not only adverse event prevention and patient safety but also focuses heavily on nurturing the clinician-patient relationship and that of the clinician and patient’s family. Virtual observation is a rare healthcare technology that can also bring immediate peace of mind and comfort by providing a continuous visual component and immediate clinician availability whenever the patient needs it.
While important to patient care, big data management is a comprehensive process that must be collected and analyzed. It is crucial in developing future healthcare processes, so proper handling from top to bottom is vital. The following from Bradley University is a close look at the pie es that can help to govern big data and its use for the most success:
Regarding recording and storing information, nurses are on the front lines. Data capture begins when a patient registers at a health care group and continues through oral medical histories, blood draws, and every other step of the episode of care. Nurses at all levels regularly record, verify, or leverage information from test results to billing codes. When these components are completed correctly, the mass quantities of data created by all the patients in an organization, or even across the country, are valuable for improving care and best practices within a group.
An article from Cleveland Clinic’s ConsultQD encourages nurses to consider several significant questions before they translate big data into research data, including the following:
- Is each variable clearly defined? If the definitions of those variables have changed over time, are those definitions available?
- Has the data been consistently and accurately recorded?
- At what rate has the data been missing from the results?
- Are compliance standards in place and rigorously followed?
Ensuring appropriate staffing levels is another area of nursing practice affected by the future of big data. Schedules continually change, and staffing requirements fluctuate with demand based on the number of patients and their needs. In most industries, if a team is short-staffed, employees do their best to make do and deal with any consequences down the line. When a nursing team is short-staffed, the situation can be a matter of life and death. Using big data, nursing leaders can more effectively determine how many staff members they need.
Adequate staffing levels also help to prevent nursing burnout. According to a 2020 survey by the UK-based Nursing Times, which took into account 91 papers written on the subject, high job demands, role conflict, and high patient complexity predicted the emotional exhaustion found in most burnout cases. This level of extreme tiredness can negatively impact patient care.
3. Evidence-Based Best Practices
When providing care to patients, nurses want to be confident that their decisions are based on the optimal treatment strategy. Big data makes it easier to determine best practices and ensure they are used within the organization. Studies have suggested that implementing evidence-based best practices has several positive benefits in clinical care, including the following:
- Improving patient outcomes
- Cutting down on unnecessary procedures
- Enhancing patient safety
This principle also impacts nursing analytics, education, and research, maximizing time and resources using the most efficient and effective practices.
With big data, nurses can use data analysis to determine the most efficient way to treat patients, from how to document their visits to the most effective way to staff a unit. This type of analysis offers robust information for creating guidelines and legislation at the federal or state level and determining how individual organizations should operate.
Big data used to analyze workflow can also provide decision support, giving nurses the confidence they need when deciding the best course of action when caring for patients.
5. New Roles
In addition to improving existing practices, big data is creating new opportunities for nurses. The growing emphasis on the collection and use of data from systems such as EHRs can already be seen within traditional positions. However, this trend is also creating several new job titles or tech-savvy nurses who want to combine their passion for the future of big data with a background in clinical care:
- Nurse informaticists: The role combines nursing practice with information and communication technology to enhance patient care. Nurse informaticists also help shape healthcare organizations’ practices and policies regarding health information technology.
- Chief nursing informatics officers: In the health care executive suite, a nurse’s emerging role is that of the chief nursing informatics officer. The CNIO is a liaison between nursing staff and information technology efforts and ensures that regulatory changes are always met.
- Clinical nurse leaders: Though clinical nurse leader is not a new position, it has evolved with the introduction of big data. Nursing professionals who wish to advance to this role will benefit from a background in informatics and other areas of data use in the clinical setting.3
The U.S. healthcare analytics market is expected to quadruple in size by the year 2030. However, many healthcare organizations are yet to be prepared for the growth. A large investment in technology and effective administrative leadership is crucial to organizations now. Patients expect to receive the best care, or best-precepted care, possible. That has always been and will never change, so naturally, analyst CS must continue to improve so that providers can present the best outcomes.
Technology in healthcare is continuing to advance and become refined, providing a wealth of knowledge in streamlined ways for the transformation of the clinician-patient relationship, as well as patient safety. Modifications happen over time in any and every industry. As analytics and readily available knowledge become more commonplace and comfortable for clinicians, we will see a difference in patient and clinician outcomes.4