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Running head: ADVERSE EVENT OR NEAR-MISS ANALYSIS 1

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

Adverse Event or Near-Miss Analysis

Learner’s Name

Capella University

Quality Improvement for Interprofessional Care

Month, Year

Comment [JS1]: This submission is
very well crafted according to the

rubric. It is written in a scholarly
voice and free of APA and

grammatical errors.

ADVERSE EVENT OR NEAR-MISS ANALYSIS 2

Adverse Event or Near-Miss Analysis

Preventable adverse events are among the top causes of death in the United States.

Estimates reveal that 210,000 to 400,000 fatal adverse events occur every year (Allen, 2013).

Examples of preventable adverse events are hospital-acquired diseases, medication errors, and

patient falls. The focus of this adverse-event analysis is medication errors, also known as adverse

drug events (ADEs), such as medication overdoses or administration of wrong medicines. The

analysis will recommend strategies to mitigate ADEs based on a case of medication overdose

observed in the emergency department (ED) at TrueWill General Hospital (TGH), a

multispecialty hospital in the United States.

A 40-year-old woman was brought to the ED after suffering a seizure. Before she was

discharged, she suffered a second seizure and the ED doctor prescribed 800 mg of phenytoin, an

anti-seizure medication, to be given intravenously (IV). The ED nurse misread the prescribed

dosage in the electronic medical record (EMR) and administered 8000 mg, which was 10 times

greater than the prescribed dosage. The patient died soon after the lethal infusion (Manias, 2012).

The incident shows that the nurse made a series of cognitive errors in medication

management and missed key steps (Manias, 2012), which will be explained in the analysis

report. Additionally, the analysis will examine the implications of adverse events on multiple

stakeholders. Relevant evidence and metrics will be incorporated when making suggestions for

improvement of patient safety at TrueWill General Hospital.

Analysis of Missed Steps Related to the Adverse Event

Emergency departments are susceptible to adverse events because of the unscheduled

nature of patient presentation, urgency, and severity of cases. In such high-pressure situations,

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ADVERSE EVENT OR NEAR-MISS ANALYSIS 3

clinicians must be more careful when treating a patient (Manias, 2012). Retracing the steps taken

by the nurse revealed several missed steps in the delivery of care.

To begin with, the drug dispensing machines in the ED were stocked with phenytoin in

250 mg vials; the correct dose required only 3.2 vials. As the nurse had misread the dose, she

needed 32 vials of the drug. She took the vials from three different drug dispensers and

administered the dose using two IV bags as well as a piggyback line (Manias, 2012). The nurse

did not question the difficulty in procuring and administering the drugs, nor did she ask anyone

to validate her calculations. Furthermore, she was not asked why she was removing so many

vials from the drug dispensers in the ED unit.

The scenario also shows that the nurse was unaware of the toxic nature of phenytoin

when administered in large quantities; she was unable to recognize the warning signs.

Additionally, the fact that the nurse could remove 32 vials is evidence of the technical drawbacks

of the automated drug-dispensing machines. The machines were not programmed to send out

alerts when large quantities of medications, especially high-alert medications like phenytoin,

were dispensed (Manias, 2012). They were also not synced to the patient’s medical record.

Therefore, the machines contained no information on drug preparation or correct dosages and did

not display any warning signs.

Various systems factors such as communication, leadership, education, training, and

innovation of health care technology influenced the ED nurse’s clinical performance. The factors

originate from the adaptation of systems theory into health care (Huber, 2017). There are,

however, areas of uncertainty regarding the factors becoming problematic in TGH’s scenario.

For example, the nurse’s hesitation to consult her team could have been caused by staff

management problems such as conflict, overwork, or shortage of ED staff. Similarly, her lack of

awareness of

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ADVERSE EVENT OR NEAR-MISS ANALYSIS 4

dosages and safety measures indicates gaps in education and training. Such problems are a result

of a breakdown of systems factors. Further evaluation is essential to understand the root causes

of adverse events and systems problems. Ignoring root causes can result in similar adverse

events in the future and negatively impact the stakeholders.

Implications of the Adverse Event on Stakeholders

Since medicine is a profession that depends on interpersonal relationships, adverse events

have emotional, psychological, and professional consequences on all stakeholders. Patients and

their families are the first victims of adverse events, while health care professionals and the

organization become the second and third victims, respectively (Mira et al., 2015). A similar

inference can be made about the adverse event at TGH; the inference is supported by certain

assumptions about the health care environment. General assumptions about health care are as

follows: (a) quality health care is a result of positive relationships among all stakeholders

(Huber, 2017); (b) stakeholders are part of a high-risk environment where errors in clinical

practice are common; (c) health care professionals are not always responsible for errors, as errors

are often caused by a breakdown in systems factors (Manias, 2012); and (d) errors diminish the

morale and job satisfaction of health care professionals and lead to more adverse events (Huber,

2017).

The analysis of implications for stakeholders begins with identifying how each category

of victims is impacted. The first victims expect hospital stays and procedures to be safe and

beneficial. When a patient suffers an injury or dies because of medical negligence, the family

may feel aggrieved and may require counseling and support. They may feel unnerved and scared

by health care professionals (Bernhard, 2013) and hesitate to seek medical treatment in the

future. The study reported that health care professionals were traumatized after committing a

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

5 ADVERSE EVENT OR NEAR-MISS ANALYSIS

preventable error or witnessing an adverse event. They may lose confidence, abandon their

careers (Bernhard, 2013), and experience anxiety or depression (Mira et al., 2015). Adverse

events are damaging to careers, and nursing professionals may face difficulty in finding another

job (Bernhard, 2013).

Adverse events also affect the organization—the third victim—by damaging its

reputation. Adverse events can discourage people from seeking treatment at a particular hospital

(Mira et al., 2015). Moreover, as most preventable errors are not covered by Medicaid and

Medicare services, the hospital may lose a significant amount of reimbursement money.

It is important that health care organizations such as TGH find ways to minimize the

impact of adverse events on stakeholders. The current trend in quality improvement

(QI) is focused on reducing human errors through automation of health care technologies. In the

case of TGH, the existing level of automation of patient records and drug dispensers is

insufficient and must be replaced. The next section recommends and discusses the benefits of a

popular QI technology—patient care dashboards.

Evaluation of Quality Improvement Technologies

Performance measurement and reporting by health care professionals are the crux of QI

because transmitting, organizing, analyzing, and displaying performance data help in identifying

areas that need improvement (Ghazisaeidi, 2015). A recent development in QI technologies is the

introduction of visual dashboards. Dashboards are interactive performance management tools

that use graphic and easy-to-use formats to present specific metrics or key performance

indicators (KPIs) on a single computer screen (Ghazisaeidi, 2015). Implementing a dashboard

can help TGH improve quality of care and patient safety.

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

ADVERSE EVENT OR NEAR-MISS ANALYSIS 6

Studies show that the use of data-driven dashboards improves patient safety and

accelerates cost-reduction efforts. A dashboard reduces human errors in processes and minimizes

the cognitive effort needed to make decisions, thereby saving time and increasing efficiency and

accuracy. The KPIs aggregate data collected from various sources. For example, clinical data

incorporated into a dashboard include patient information gathered from physician or nurse

charts. A dashboard can also consolidate metrics about market dynamics, innovation for long-

term sustainability, and availability of financial and human resources for managers to analyze

(Weiner, Balijepally, & Tanniru, 2015).

To help TGH efficiently customize the dashboard to its specific clinical context, the tool

should be tested and evaluated using certain criteria. The categories for each criterion are as

follows: (a) easy customization; (b) knowledge discovery; (c) security; (d) information delivery;

(e) visual design; (f) alerts; and (g) system connectivity and integration (Karami, 2014). These

criteria can be used for all types of dashboards and health care settings.

While the design features are important, the dashboard is only useful if the KPIs provide

valuable data. Hence, the selection and development of KPIs are critical steps in QI at TGH

without which the organization risks ignoring areas that require corrective action

(Ghazisaeidi, 2015).

Relevant Metrics of Quality Improvement for TrueWill General Hospital

The KPIs are the most valuable content in a dashboard. They measure performance

across the organization using a combination of administrative and clinical data sets. To prevent

overloading the electronic dashboard, only a limited number of KPIs concerning high-priority

areas is selected. These KPIs are based on evidence-based academic literature. Data for each KPI

is sourced from different source systems in the organization such as the accounting system,

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

7 ADVERSE EVENT OR NEAR-MISS ANALYSIS

human resources system, and clinical system (Ghazisaeidi, 2015). For example, clinical data are

sourced from reports on whether clinicians treated the correct patient, addressed the equipment

or supplies needed, prescribed the correct medication or anesthesia at the appropriate time, and

detected patient allergies (Hagland, 2012). For the adverse event analysis report, the relevant

KPIs will focus on clinical and patient-centric metrics.

Health care agencies such as the Agency for Healthcare Research and Quality (AHRQ)

have developed their own metrics that address various aspects of quality: patient safety,

prevention quality, inpatient quality, and pediatric quality. TGH can customize its clinical and

patient-centric KPIs for the dashboard from these aspects. Examples of relevant AHRQ metrics

that are applicable to the ED adverse event include (a) death rate in low-mortality-diagnosis-

related groups; (b) accidental puncture or laceration rate; (c) heart failure mortality rate; and (d)

dehydration admission rate (AHRQ, 2015a, 2015b, 2015c).

The ED department at THG can include other relevant KPIs in the dashboard such as (a)

monthly averages for patient length of stay (inpatient and outpatient); (b) patients in the ED who

left without being seen (monthly); (c) radiology test (CT scan and x-ray), start to final dictation

turnaround time (Weiner, Balijepally, & Tanniru, 2015); (d) speed of onset of pain relief; (e)

cost-reduction percentage per patient; and (f) risk of drug interactions (Dolan, Veazie, & Russ,

2013).

The evidence base for the selected KPIs consists of peer-reviewed studies. Hagland

(2012) proved the success of the dashboard for patient safety optimization at the Saint Luke’s

Mid America Heart Institute, Missouri. The dashboard increased communication within medical

teams, reduced safety errors, and improved coordination between the teams. Dolan, Veazie, and

Russ (2013) studied the effectiveness of the electronic dashboard as a decision-making tool. The

results showed that the dashboard had potential to foster informed decision making and patient-

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

ADVERSE EVENT OR NEAR-MISS ANALYSIS 8

centered care. Weiner, Balijepally, and Tanniru (2015) studied the integration of data-driven

dashboards at the St. Joseph Mercy Oakland Hospital in Michigan. The study reported tangible

benefits such as KPIs reporting reduced adverse event rates and intangible benefits such as

increased accountability across the organization, self-improvement among nurses, and improved

unit performance.

The dashboard is just the technological component of quality improvement. TGH

requires a broader QI framework that incorporates organizational strategies to overcome

problems in the ED that resulted in the death of the patient. A suitable framework will be selected

after evaluating different perspectives and data about quality improvement.

Outline for a Quality Improvement Initiative for TrueWill General Hospital

The health care industry has adopted many QI and measurement models over the years.

Two popular models in quality improvement are the Six Sigma and LEAN models. Both models

have similar goals: eliminate operational waste and defects to improve quality and efficiency of a

system. The main difference between Six Sigma and LEAN is in the approaches to identifying

causes of defects and errors. According to Six Sigma, variations in processes cause errors, while

LEAN thinking highlights unnecessary steps as the cause of operational waste and errors

(AHRQ, 2017).

As both process variations and unnecessary steps can cause errors, the combination of the

LEAN and Six Sigma models can be implemented at TGH as its quality improvement outline.

The hospital can follow the LEAN Six Sigma model’s DMAIC approach. DMAIC is a five-step

approach to process improvement: (a) define—identify key business issues; (b) measure—

understand current levels of performance; (c) analyze—identify root causes of process errors; (d)

improve—introduce strategies and tools to improve quality of process; and (e) control—maintain

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

9 ADVERSE EVENT OR NEAR-MISS ANALYSIS

new levels of performance across the organization (Huber, 2017). Implementing the LEAN Six

Sigma into all units and departments—not just the ED—at TGH will help streamline processes

proactively. By improving the whole system, the hospital can prevent communication gaps or

errors, disorganization, and breakdown of faulty systems. DMAIC steps will allow TGH to

enhance QI process using tools and strategies such as the dashboard.

The Institute of Health Improvement’s Plan-Do-Study-Act (PDSA) model and the

Baldrige criteria were other quality improvement perspectives that were considered (Huber,

2017). However, the PDSA insufficiently addressed specific types of errors caused by variations

or unnecessary steps, unlike the LEAN Six Sigma model. The Baldrige criteria too were

insufficient because their usage was more suitable for enabling educational excellence.

Additionally, there is extensive evidence supporting the LEAN and Six Sigma models in quality

improvement.

While the LEAN Six Sigma model and dashboards have a high success rate,

implementing the QI initiative depends on coordinated and collaborative efforts by multiple

stakeholders. Teamwork enables TGH’s health care professionals to optimize systems factors

and the quality of processes and prevent future adverse events.

Conclusion

The process of QI and ensuring patient safety is challenging because health care

organizations must simultaneously provide the highest quality of services and introduce cost-

reduction strategies. Quality improvement initiatives such as implementing dashboards must

focus on finding and fixing the root causes of errors or process inefficiencies. To identify the

root causes of errors, the organization should train health care professionals, update health care

technologies, and open lines of communication to meet the expectations of patients for safe,

timely, affordable, and quality care.

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10

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ADVERSE EVENT OR NEAR-MISS ANALYSIS

References

Agency for Healthcare Research and Quality. (2015a). Prevention quality indicators. Retrieved

from https://qualityindicators.ahrq.gov/Downloads/Modules/PQI/V50/PQI_Brochure.pdf

Agency for Healthcare Research and Quality. (2015b). Patient safety indicators. Retrieved from

https://qualityindicators.ahrq.gov/Downloads/Modules/PSI/V50/PSI_Brochure.pdf

Agency for Healthcare Research and Quality. (2015c). Inpatient quality indicators. Retrieved

from https://qualityindicators.ahrq.gov/Downloads/Modules/IQI/V50/IQI_Brochure.pdf

Agency for Healthcare Research and Quality. (2017). Section 4: Ways to approach the quality

improvement process. In The CAHPS ambulatory care improvement guide: Practical

strategies for improving patient experience. Retrieved from

https://ahrq.gov/cahps/quality-improvement/improvement-guide/4-approach-qi-

process/sect4part2.html#4c

Allen, M. (2013, September 19). How many die from medical mistakes in U.S. hospitals?

[Ongoing investigative report]. ProPublica. Retrieved from

https://propublica.org/article/how-many-die-from-medical-mistakes-in-us-hospitals

Bernhard, B. (2013, May 5). Medical errors leave devastating impact on families, professionals.

St. Louis Post-Dispatch. Retrieved from http://stltoday.com/lifestyles/health-med-

fit/health/medical-errors-leave-devastating-impact-on-families-

professionals/article_0cb6f031-fbc6-5b8f-bed9-610163dbf2f8.html

Dolan, J. G., Veazie, P. J., & Russ, A. J. (2013). Development and initial evaluation of a

treatment decision dashboard. BMC Medical Informatics and Decision Making, 13(1), 51.

Retrieved from https://search-proquest-com.library.capella.edu/docview/1347649264?pq-

origsite=summon

ADVERSE EVENT OR NEAR-MISS ANALYSIS 11

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

Hagland, M. (2012). A dashboard for OR patient safety optimization. Healthcare

Informatics, 29(8), 29–31. Retrieved from https://search-proquest-

com.library.capella.edu/docview/1038458450?pq-

origsite=summon&http://library.capella.edu/login%3furl=accountid=27965

Huber, D. L. (2017). Leadership and nursing care management (6th ed.) Philadelphia: W.B.

Saunders. http://dx.doi.org/10.7748/nm.21.6.13.s14

Ghazisaeidi, M., Safdari, R., Torabi, M., Mirzaee, M., Farzi, J., & Goodini, A. (2015).

Development of performance dashboards in healthcare sector: Key practical issues. Acta

Informatica Medica, 23(5), 317–321. Retrieved from https://search-proquest-

com.library.capella.edu/docview/1727377974?pq-origsite=summon

Karami, M. (2014). A design protocol to develop radiology dashboards. Acta Informatica

Medica, 22(5), 341–346. http://dx.doi.org/10.5455/aim.2014.22.341-346

Manias, E. (2012). Looking for meds in all the wrong places [Case study commentary].

Retrieved from https://psnet.ahrq.gov/webmm/case/282/looking-for-meds-in-all-the-

wrong-places?q=Looking+for+meds+in+all+the+wrong+place

Mira, J. J., Lorenzo, S., Carrillo, I., Ferrús, L., Pérez-Pérez, P., Iglesias, F.,… Astier, P. (2015).

Interventions in health organisations to reduce the impact of adverse events in second and

third victims. BMC Health Services Research, 15(1), 341–350. Retrieved from

https://search-proquest-com.library.capella.edu/docview/1780186926?pq-

origsite=summon&http://library.capella.edu/login%3furl=accountid=27965

Weiner, J., Balijepally, V., & Tanniru, M. (2015). Integrating strategic and operational decision

making using data-driven dashboards: The case of St. Joseph Mercy Oakland

Hospital. Journal of Healthcare Management, 60(5), 319–331. Retrieved from

Comment [JS2]: I would suggest
locating a more current reference.

This reference is on the cusp of being

outdated according to health care

research standards of being less than

five years. With this topic, I am sure
there are more updated references that

could be used instead.

Comment [JS3]: This is another
reference that should be updated for

the above reasons.

ADVERSE EVENT OR NEAR-MISS ANALYSIS 12

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com.library.capella.edu/docview/1733617419?OpenUrlRefId=info:xri/sid:summon&acco

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