Moving evidence into practice with outcomes-driven acuity data
Intended for healthcare professionals
Evidence and practice    

Moving evidence into practice with outcomes-driven acuity data

Karla Rae Ure Clinical Informatics Analyst, Intermountain Health Care, Salt Lake City, Utah, US
Trevor Hightower Clinical Informatics Analyst, Intermountain Health Care, Salt Lake City, Utah, US
Durenda Juergensen Chief Nursing Officer, health system operations, Cerner, North Kansas City, Missouri, US
Robert Lovett Senior Solution Strategist, health system operations, Cerner, North Kansas City, Missouri, US

Why you should read this article:
  • To recognise that outcomes-driven acuity data provide a valid, objective and reliable measure of patient acuity and workload to support decisions about staffing

  • To acknowledge the critical need for nursing data to be used in the care process provided by nurses

  • To identify the requirement for nursing leaders to align the right number of resources, skills and competencies to individual patient care

This article describes how driving evidence-based practice across the paediatric population using outcomes-driven acuity technology led to the formulation of a combined multihospital and health information technology acuity council. The cross-collaboration among acuity experts resulted in a pilot project being undertaken, implementing evidence-based practice using acuity data and expanding newborn and post-partum acuity outcome sets. The newborn acuity outcome set was expanded from four to seven outcomes, and the post-partum outcome set from nine to 12 outcomes. The revised outcome sets facilitate implementation of evidence-based practice to evaluate the effect of nursing care and practice on patient outcomes.

Nursing Management. doi: 10.7748/nm.2020.e1968

Peer review

This article has been subject to external double-blind peer review and has been checked for plagiarism using automated software

Correspondence

karla.ure@imail.org

Conflict of interest

None declared

Ure KR, Hightower T, Juergensen D et al (2020) Moving evidence into practice with outcomes-driven acuity data. Nursing Management. doi: 10.7748/nm.2020.e1968

Published online: 23 November 2020

Introduction

The rate of perceived exertion (RPE) is a reliable measure of heart rate and perceived exertion for self-monitoring of exercise intensity (Naclerio et al 2015, Tang et al 2016). It provides insights into the intensity of exercise to regulate resistance and the overall effect of training to influence the perception of effort during exercise (Naclerio et al 2015). The use of subjective data with RPE supports athletes to measure the intensity of exercise and move to the next level of fitness.

Nurses rely on and use objective data to drive decisions and evaluate outcomes to move patients to the next level of wellness. Nurses capture objective data in the electronic medical record to document the patient’s clinical picture. They use this objective data to support their ethical and social responsibility to evaluate the effect of nursing care and practice on patient outcomes (American Nurses Association (ANA) 2015).

Nurses also use this objective data to measure acuity (intensity) to capture the precise care hours needed for patient care transitions to the next level of wellness (Garcia 2017, Garcia and Lovett 2018). Quantifying clinical data using the electronic medical record, nursing taxonomy and computer algorithms enables rapid analysis of documentation to automate an objective and accurate calculation of acuity (Garcia 2017, Garcia and Lovett 2018). Objective, outcomes-driven acuity calculated in this fashion can enable an estimate to be made of the number of nursing care hours (workload) required to optimise a patient’s ability to improve (Garcia 2017).

The effect on patient outcomes of outcomes-driven acuity data is a valid, objective and reliable measure of patient acuity and staff workload for decision support such as staffing, patient assignment equity and length of stay care transitions (Nguyen 2015, Garcia 2017, Trepanier et al 2017, Garcia and Lovett 2018). Fundamentally, by using acuity measurement, nurses shift their focus to what they achieve instead of the tasks accomplished (Jones 2016).

Nurses also focus and rely on a strong organisational foundation to support and use evidence-based practice to monitor outcomes. It means evidence-based practice moves from clinically based to include a holistic, problem-solving approach to clinical decision-making using evidence to provide strategies to improve outcomes (Mackey and Bassendowski 2017, Moorhead 2019, Ost et al 2020). Organisations can achieve the alignment of best practice with the consistent implementation of evidence-based practice to improve outcomes and patient care delivery (Ost et al 2020).

This article explores how formulating a nursing paediatric acuity council, conducting a pilot project and evaluating data for decision support provided an opportunity for organisations and healthcare technology acuity experts to collaborate and drive changes through evidence.

Key points

  • The use of clinical data contained in the electronic medical record enables objective and reliable acuity and workload calculations to support data-driven decisions for nursing operations

  • Nursing leaders can embrace evidence-based practice using acuity for decision support to align resources, skills and competencies to individual patient care

  • The use of outcomes-driven acuity data means that nurses focus on what they achieve rather than task-based care

Creating a paediatric acuity council

Ensuring standards of care across a patient population was a catalyst to formulate a paediatric acuity council to review and revise standard paediatric acuity outcome sets. The council included paediatric clinical and acuity experts from five large academic and community paediatric institutions in the US. It included acuity experts from healthcare technology and nursing outcomes classification experts from the University of Iowa. Each paediatric institution used the same outcomes-driven acuity technology (Cerner Clairvia), which is agnostic (compatible with many types) to the electronic medical record and enhanced collaboration among members using different electronic medical records.

Outcome sets derived from nursing outcomes classification domains, classes and outcomes provide the taxonomy to develop a holistic patient picture for any population (Moorhead et al 2018). The paediatric patient’s needs are complex. Nurses use outcomes-driven acuity standard outcomes for paediatric, post-partum, neonatal intensive care unit and newborn patients to accurately assess the patient’s complexity. The review and revision of the standard outcome sets involved an analysis of each paediatric acuity council member’s evidence-based practices, acuity data, inter-rater reliability of acuity and documentation practices. The analysis provided an opportunity to capture the paediatric outcome set accurately while sustaining inter-rater reliability (86% or higher) to prevent a mismatch of outcomes to patient population and acuity measurement to measure and represent the paediatric population (Garcia 2017, Garcia and Lovett 2018). As a result, the outcomes data used in post-partum and newborn patients revealed an opportunity for using evidence to drive outcome changes.

The pilot project

The paediatric acuity council post-partum and newborn data analysis revealed that deviations in acuity would lead to potentially false indications of high acuity levels, since information gathered was collected in smaller outcome sets. The council recommended changing outcome sets and conducting a pilot project. The purpose of the pilot project was to evaluate inter-rater reliability, acuity and workload data to revise and obtain more appropriate outcome sets for the post-partum and newborn patient populations. The pilot organisation chose three maternal-child units to participate in a pilot project. The units identified were from facilities of various sizes in an extensive health system with diverse patient demographics.

The pilot project followed a plan-do-study-act (PDSA) approach for quality improvement. PDSA is a four-step process for the pilot organisation to strategise (plan), implement (do), monitor results (study) and educate (act) on what was learned (Dawson 2019). The institutional review board at the pilot organisation was consulted on the project. The pilot project involved implementation of existing knowledge using evidence-based practice and was not considered human subject research, thus it was approved by the institutional review board.

Conducting the pilot project

Before beginning the pilot project, a team of clinical informaticists and bedside clinical staff was formed at a paediatric acuity council member facility. The team retrieved pilot information from outcomes-driven acuity technology to evaluate the baseline data from the previous month. The data were directly extracted through the outcomes-driven acuity technology using reports to summarise the nursing outcomes classification outcome ratings, assessment compliance completion, acuity summary and acuity validation. There were no alterations when retrieving data from the outcomes-driven acuity technology or the reports produced. The baseline data analysis revealed that the level of acuity was not reflective of the well newborn and post-partum population, which is an acuity level of 1-5. After sharing the baseline data analysis, the paediatric acuity council determined that a pilot to evaluate the expansion of the newborn and post-partum outcome sets was needed.

Four main outcomes-driven acuity reports provided the data enabling refinement of the outcomes needed to fully capture a holistic picture of the patient throughout the pilot.

First, the outcomes rating summary report provided a snapshot of the outcomes used over a selected date range. In addition, the report provided the number and percentage of assessments that fell into a Likert rating (0 to 5), which identified actions needed on outcomes. Actions included analysis of appropriate patient population outcomes, investigating for a mismatch in clinical documentation or a knowledge deficit related to documentation of the specific outcome in the electronic medical record.

The outcomes-driven acuity technology requires an assessment documentation threshold of 75% or higher to calculate an objective acuity level. A document threshold is the minimum threshold or required clinical documentation to calculate an appropriate acuity level.

The outcomes-driven acuity technology has a threshold requiring 75% or more of assessment documentation to be completed to calculate an objective acuity level. The second report used was the assessment percentage complete report, indicating the total number of assessments and the corresponding levels of completion. If a unit was consistently below the 75% threshold, an investigation took place to determine the cause and corrections were made.

The third report used was the acuity summary report, which summarised acuity data (levels, assessments conducted and assessment complete percentage) over a date range for the selected units on a graph. A graph showing a normal distribution bell curve indicated that documentation was consistent and timely, and patients on the unit had similar but variable needs. A graph with a bell curve skewed to the right, skewed to the left or a bimodal curve indicated variants to acuity distribution, and that more patients had a higher acuity level or a lower acuity level than the expected acuity level , or there were dual populations. Variants to acuity distribution indicated the need for further investigation and for a review of the outcomes.

The fourth report used was the acuity validation report, which gave the acuity weighted average score, acuity level and outcomes ratings for each completed patient assessment. The report validated whether acuity assessments accurately measured each patient’s acuity level to ensure an accurate picture of the patient.

The pilot involved implementing the revised newborn and post-partum outcome sets from the paediatric acuity council with a delay in the implementation of the Knowledge: Infant Care outcome (Moorhead et al 2018). The pilot site was reluctant to add the outcome when the pilot started because of changes to charting practices. Charting practices changed to include educational items and non-essential areas to capture the information needed for the outcome.

During the pilot, the baseline information was compared weekly with the outcomes-driven acuity reports from the three pilot units. Weekly updates on the pilot status were sent to the units and reviewed with the clinical and informatics teams. Weekly meetings reviewed acuity level trends, acuity assessment completion rates and the appropriate spread of values for each outcome in the outcome sets. Adjustments in the outcome sets occurred according to the outcomes-driven acuity report’s data and input from the pilot units. Notably, patient-protected health information was not used in the outcomes-driven acuity reports or used to support the pilot’s data decisions.

Figure 1 displays overall acuity levels. The baseline average acuity level was 3.78 and the post-pilot average acuity level was 3.27. The acuity level dropped initially with implementation, which was expected. The subsequent stabilisation occurred due to the inclusion of additional outcomes to reflect more precisely the patient population. Once the data stabilised, the pilot concluded.

Figure 1.

Overall average acuity levels

nm.2020.e1968_0001.jpg

Evaluating the data

After the first two weeks of the pilot, the team added the outcome Knowledge: Infant Care to the newborn outcome set (Moorhead et al 2018). The implementation of the outcome required nurses to chart teaching in the mother’s and newborn’s chart to capture the required data. After six weeks, the outcomes-driven acuity data indicated that this outcome consistently held a high percentage of data not coming in, which suggested that nurses were not charting teaching in the newborn’s chart, only in the mother’s chart. Ultimately, the Knowledge: Infant Care outcome was removed and replaced with Functional Health: Cardiopulmonary Status. The change provided cardiopulmonary data from the Newborn Adaptation outcome to capture the newborn’s transition to life (Moorhead et al 2018).

Decisions to alter the outcome sets were made by studying the acuity data with the pilot units. For example, the acuity assessment completions (Figure 2) show an expected decrease in the percentage of complete acuity assessments during implementation. The decrease occurred due to changing the outcomes and the initial processing of the acuity levels. The baseline levels returned after the first week.

Figure 2.

Acuity assessment completion

nm.2020.e1968_0002.jpg

The acuity data used throughout the pilot provided insights on any acuity variances per outcome and overall acuity level trends for the team. The data in Figures 3 and 4 provided monitoring of acuity level trends. Figure 3 provides the average mother acuity levels with standard deviation for the three units over the pilot. A slight drop occurred due to increased outcomes in the post-partum outcome set, then consistent levels stabilised. Figure 4 provides the average newborn acuity levels with standard deviation for the three units over the pilot. An expected drop in acuity levels occurred due to the increased outcomes in the set. Levels stabilised over the pilot. Acuity levels changed as the outcome sets were revised to better capture the newborn patient population.

Figure 3.

Mother acuity levels with standard deviation

nm.2020.e1968_0003.jpg
Figure 4.

Newborn acuity levels with standard deviation

nm.2020.e1968_0004.jpg

As a result of monitoring and analysing the outcomes-driven acuity data, the pilot expanded the newborn outcomes from the initial four, which were:

  • Breastfeeding Establishment: Infant.

  • Discomfort Level.

  • Infection Severity.

  • Newborn Adaptation.

The pilot indicated a need to expand this to seven by adding the following:

  • Parent-Infant Attachment.

  • Thermoregulation.

  • Tissue Integrity: Skin and Mucous Membranes.

In addition, the pilot data indicated outcomes lacking documentation to calculate an expected acuity level. Therefore, the standard ‘Breastfeeding Establishment: Infant’ outcome was changed to align with the NICU outcome set to include ‘Nutritional Status: Nutrient Intake’. The pilot data also indicated the ‘Knowledge: Infant Care’ outcome documentation was insufficient, so the ‘Cardiopulmonary Status’ outcome replaced it, and calculated acuity appropriately reflecting the patient population.

The pilot also expanded the post-partum outcome set from the initial nine outcomes, which were:

  • Breastfeeding Establishment: Maternal.

  • Discomfort Level.

  • Family Support During Treatment.

  • Infection Severity.

  • Knowledge: Treatment Regimen.

  • Maternal Status.

  • Parent-Infant Attachment.

  • Self-care: Activities of Daily Living.

  • Safe Healthcare Environment.

Three outcomes were added (Moorhead et al 2018):

  • Coping.

  • Nutritional Status: Food and Fluid Intake.

  • Personal Health Status.

Overall, the revised newborn and post-partum outcome sets were easily implemented with education and sharing the outcomes-driven acuity data with the staff of the three pilot units. Feedback from the staff indicated that the revised outcome sets provided a more comprehensive patient acuity. In addition, there was consistent completion of data pulling into the outcomes, with the appropriate spread of acuity in the individual outcomes.

In Figures 5 and 6 the data demonstrate the percentage spread of newborn and mother acuity levels respectively, and the shift to the left in these figures was expected.

Figure 5.

Newborn acuity levels with standard deviation

nm.2020.e1968_0005.jpg
Figure 6.

Overall mother acuity summary

nm.2020.e1968_0006.jpg

The three pilot criteria for success were the overall average acuity level between 3.20 and 3.28, the acuity assessment completion rate of more than 95%, and assessment scores stabilised. The newborn acuity levels shifted from a high percentage of inappropriately high acuity levels to an appropriate spread of acuity levels. The pilot project was reviewed and discussed with the full paediatric acuity council by the facility pilot team. The subsequent outcome sets were revised for post-partum and newborn populations.

Limitations

One limitation was the patient census (total number of patients on a unit) on the pilot units. The patient census level was lower than average for some units and may have affected the patient sample. A lower census, or smaller patient population, can change the overall average patient acuity due to the sample size for the population.

A second limitation was related to the model of care and charting requirements. The different models of care (mother-baby or paediatric only) affected the electronic medical record documentation fields, primarily when documenting teaching. For example, in the mother-baby model, all teaching documentation was done in the mother’s chart versus both baby and mother’s charts. However, in a paediatric-only model, teaching documentation occurs on the baby’s electronic medical record by charting the parent or provider’s comprehension.

The third limitation was the technology adoption, affected by the end-user’s willingness to embrace the acuity methodology and technology. Often, embracing acuity methodology and technology relies on refining the documentation practices of nurses and working collaboratively. The process includes ensuring correct mapping of the electronic medical record data to the outcomes-driven acuity outcome sets, and real-time feedback to nursing colleagues facilitates alignment with documentation standards (Garcia 2017).

Nurses’ call to action

One of the action steps that nurses can take includes embracing evidence-based practice to support acuity for decision support. In the value-based healthcare environment, nursing leaders are increasingly aligning the right number of resources, skills and competencies to individual patient care need using acuity to facilitate quality outcomes (Trepanier et al 2017). Therefore, ensuring a sustainable inter-rater reliability of more than 86% provides data for decision support in nursing operations (Garcia 2017, Garcia and Lovett 2018). Nurse leaders can then develop the interconnections between safe staffing, quality, safety and the patient experience through outcome measurement and standardisation (Fitzpatrick 2017, ANA 2020).

A bedside clinician thinks differently than a nurse leader about acuity and the data. A bedside clinician thinks about the acuity level and nurse-patient assignments. Using specific patient population outcome sets enables precise measurement of acuity from electronic medical record documentation (Nguyen 2015, Garcia and Lovett 2018). Then, using and trusting the acuity data produced supports nurse-patient assignment equity and enhances the value of the data for the bedside clinician. Furthermore, the bedside clinician develops trust in the data by nurse-nurse and nurse-computer inter-rater reliability maintained at 86% agreement using a peer review process serving as a unit clinical expert (Garcia 2017).

Nursing informaticists must focus on and analyse documentation to ensure the correct outcomes support nursing documentation. Furthermore, by working in partnership with unit clinical experts, a unique collaboration occurs between clinical care delivery and nursing informatics through acuity data. An important benefit to using a data-rich approach in nursing workflows is decreasing the burden of nurses collecting additional data manually to support staffing and assignments (Welton 2017).

Conclusion

The formulation of a paediatric acuity council comprising clinical experts across the US using the same outcomes-driven acuity technology enabled revision of paediatric outcome sets. In addition, the revised outcome sets more accurately reflect the patient populations and enable evidence-based practice to be implemented. After all, patients are not end-users of acuity data or evidence-based practice; rather they are beneficiaries of both.

References

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