Description
Assessment 1
NURS-FPX6016 – Prepare an analysis (5–7 pages) of an adverse event or a near miss from your professional nursing experience and outline a QI initiative that would address it.
Adverse Event
In a hospital labor and delivery unit, a laboring mother experienced a postpartum hemorrhage that resulted in an emergency hysterectomy due to late recognition of actual blood loss. The unit instituted measures for early recognition of at-risk patients and developed protocols to address it. These protocols included:
Pre-admission assessment of risks for a postpartum hemorrhage
Continued risk assessments based on changes in status
Open communication with all team members and agreement on level of risk
Communication with hospital blood bank and placement of units of blood on hold
Placement of second IV access on patient in case of transfusion need
Creation of “Code OB” Box with all necessary medications for hemorrhage
Creation of Hemorrhage Cart with all necessary emergency supplies
Inservice with all unit staff on process to follow during a hemorrhage
Practice drills involving all unit staff on steps to follow during hemorrhage
Development of Hemmorrhage algorithm to follow and information binder
Health care organizations strive to create a culture of safety. Despite technological advances, quality care initiatives, oversight, ongoing education and training, legislation, and regulations, medical errors continue to be made. Some are small and easily remedied with the patient unaware of the infraction. Others can be catastrophic and irreversible, altering the lives of patients and their caregivers and unleashing massive reforms and costly litigation. Many errors are attributable to ineffective interprofessional communication.
The goal of this assessment is to allow you to focus on a specific event in a health care setting that impacts patient safety and related organizational vulnerabilities and to propose a QI initiative to prevent future incidents. It will give you the chance to develop your analytical skills in the problem-solving contexts you likely find yourself in as a health care professional.
Health care organizations strive for a culture of safety. Yet, despite technological advances, quality care initiatives, oversight, ongoing education and training, laws, legislation, and regulations, medical errors continue to occur. Some are small and easily remedied with the patient unaware of the infraction. Others can be catastrophic and irreversible, altering the lives of patients and their caregivers and unleashing massive reforms and costly litigation.
Historically, medical errors were reported and analyzed in hindsight. Today, QI initiatives attempt to be proactive, which contributes to the amount of attention paid to adverse events and near misses. Backed up by new technologies and reporting metrics, adverse events and near misses can provide insight into potential ways to improve care delivery and ensure patient safety.
For clarification, the National Quality Forum (n.d.) defines the following:
Adverse event: An event that results in unintended harm to the patient by an act of commission or omission rather than by the underlying disease or condition of the patient.
Near miss: An event or a situation that did not produce patient harm, but only because of intervening factors, such as patient health or timely intervention.
Prepare a comprehensive analysis of an adverse event or a near miss from your professional nursing experience that you or a peer experienced. Provide an analysis of the impact of the same type of adverse event or near miss in other facilities. How was it managed, who was involved, and how was it resolved? Be sure to:
Analyze the implications of the adverse event or near miss for all stakeholders.
Analyze the sequence of events, missed steps, or protocol deviations related to the adverse event or near miss using a root cause analysis.
Evaluate QI actions or technologies related to the event that are required to reduce risk and increase patient safety.
Evaluate how other institutions integrated solutions to prevent these types of events.
Incorporate relevant metrics of the adverse event or near miss to support need for improvement.
Outline a QI initiative to prevent a future adverse event or near miss.
Ensure your analysis conveys purpose, in an appropriate tone and style, incorporating supporting evidence and adhering to organizational, professional, and scholarly writing standards.
Be sure your analysis addresses all of the above points. You may also want to read the Adverse Event or Near Miss Analysis Scoring Guide to better understand the performance levels that relate to each grading criterion. Additionally, be sure to review the Guiding Questions: Adverse Event or Near Miss Analysis [DOCX] Download Guiding Questions: Adverse Event or Near Miss Analysis [DOCX]document for additional clarification about things to consider when creating your assessment.
Guiding Questions
Adverse Event or Near Miss Analysis
This document is designed to give you questions to consider and additional guidance to help you successfully complete the Adverse Event or Near Miss Analysis assessment. You may find it useful to use this document as a prewriting exercise, an outlining tool, or a final check to ensure you have sufficiently addressed all the grading criteria for this assessment. This document is a resource to help you complete the assessment. Do not turn in this document as your assessment submission.
For examples of adverse events or near misses, visit:
Agency for Healthcare Research and Quality. (2021). WebM&M cases & commentaries. https://psnet.ahrq.gov/webmm
Analyze the implications of the adverse event or near miss for all stakeholders.
●What are the possible short-term and long-term effects on the stakeholders (patient, family, interprofessional team, facility, community, et cetera)?
● What are the responsibilities and actions of the interprofessional team related to the adverse event or near miss?
● What measures should have been taken? Who are the responsible parties or roles?
● How did the incident impact the stakeholders? Did it change how they do their work, or how or what they report? Analyze the sequence of events, missed steps, or protocol deviations related to the adverse event or near miss using a root cause analysis.
● How did the event result from a patient’s medical management rather than from the underlying condition?
● What were the missed steps or protocol deviations that led to the adverse event or near miss? What was overlooked? Why?
● What kind of interprofessional communications could have prevented this event?
● To what extent was the adverse event or near miss preventable?
Evaluate quality improvement actions or technologies related to the event that are
required to reduce risk and increase patient safety.
● What quality improvement technologies are in place to increase patient safety and
reduce risks that pertain to this adverse event? What would prevent it from happening in the future?
● Are those technologies being utilized appropriately? How could they be more usefully employed?
● How do other institutions prevent these types of events from occurring?
● What data are generated from the facility’s dashboard related to the selected incident? (By dashboard, we mean the data that are generated from the information technology platform that provides integrated operational, financial, clinical, and patient safety data for health care management.
● What data are associated with the adverse event or near miss? What do the relevant metrics show? (Patient satisfaction and readmission rates are important metrics. Look at trending data and compare to see where relevant metrics are headed.)
● What research or data related to the adverse event or near miss is available outside of your institution?
● Compare internal data to external data. What do you find?
Outline a quality improvement initiative to prevent a future adverse event or near miss based on research and evidence-based practices.
● How was the incident managed and monitored in the selected institution?
● What quality improvement initiatives have been shown to work? Why are they
successful? What is the evidence?
● What elements can be applied to prevent future adverse events or near misses?
Convey purpose, in an appropriate tone and style, incorporating supporting evidence
and adhering to organizational, professional, and scholarly writing standards.
● Is your analysis logically structured?
● Is your analysis 5–7 double-spaced pages (not including title page and reference list)?
● Is your writing clear and free from errors?
● Does your analysis include both a title page and reference list?
● Did you use a minimum of three sources? Were they published within the last five
years?
● Are they cited in current APA format throughout the plan?
Your assessment should also meet the following requirements:
Length of submission: A minimum of five but no more than seven double-spaced, typed pages, not including the title page or References section.
Number of references: Cite a minimum of three sources of scholarly or professional evidence that support your evaluation, recommendations, and plans. Current source material is defined as no older than five years unless it is a seminal work. Review the Nursing Master’s Program (MSN) Library Guide for guidance.
APA formatting: Resources and citations are formatted according to current APA style. Review the Evidence and APA section of the Writing Center for guidance.
By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and scoring guide criteria:
Competency 1: Plan quality improvement initiatives in response to adverse events and near-miss analyses.
Analyze the implications of an adverse event or a near miss for all stakeholders.
Analyze the sequence of events, missed steps, or protocol deviations related to an adverse event or a near miss using a root cause analysis.
Outline a quality improvement initiative to prevent a future adverse event or near miss based on research and evidence-based practices.
Competency 3: Evaluate quality improvement initiatives using sensitive and sound outcome measures.
Evaluate and identify quality improvement actions or technologies related to an event that are required to reduce risk and increase patient safety.
Competency 5: Apply effective communication strategies to promote quality improvement of interprofessional care.
Convey purpose, in an appropriate tone and style, incorporating supporting evidence and adhering to organizational, professional, and scholarly writing standards.
Reference
National Quality Forum. (n.d.). NQF patient safety terms and definitions. http://www.qualityforum.org/Topics/Safety_Definiti…
The attached resources delve not only into the promise of predictive analytics, algorithms, machine learning, and other technologies, but also the ethical and practical questions they raise. This will be important for how you approach hazards and adverse events in Assessment 1.
Resources:
The Postpartum Hemorrhage Patient Safety Bundle Implementation at a Single Institution: Successes, Failures, and Lessons Learned
Christina M. Duzyj, Carla Boyle, Kathleen Mahoney,Anna Rose Johnson, Grace Ogot, Charletta Ayers
Abstract
Objective In 2015, a multidisciplinary consensus bundle of recommendations for the anticipation and management of postpartum hemorrhage was published. Our goal was to evaluate the successes and failures of our institutional bundle implementation process.
Study Design An interdisciplinary committee was created to facilitate bundle implementation. All components of the bundle were addressed with cross-disciplinary teaching between stakeholders on the obstetrics units. Tools were built in the electronic medical record to facilitate bundle components of risk stratification, quantitative blood loss calculation, and stage-based hemorrhage management. Bundle components were individually evaluated for acceptability and sustainability. Overall rates of hemorrhage and transfusion from the periods 1 year before and after bundle implementation were also evaluated.
Results Readiness bundle components were successfully implemented, although simulation drills demonstrated limited sustainability. Recognition components were mixed: risk stratification was successfully and sustainably implemented while quantitative blood loss met resistance and was ultimately discontinued as it did not clinically perform superiorly to estimated blood loss. Among response and reporting elements, patient level support and team debriefing were noted as particular deficiencies in our program.
Conclusion The postpartum hemorrhage patient safety bundle provided concrete individual elements, which overall improved the success of a stratified program implementation. Multiple deficiencies in acceptability and sustainability were uncovered during our process, particularly concerns about quantitative blood loss implementation and team communication skills.
Key Points
Supply readiness and protocol development were “quick wins.”
Culture change elements included recognition, response, and communication.
Dedicated champions and electronic medical record tools improved sustainability.
Poor acceptability and lack of improved outcomes led to element failure.
Keywords
postpartum hemorrhage – patient safety – safety bundle – electronic medical record – estimated blood loss – quantitative blood loss
Hidden Danger! Insidious Postpartum Bleeding After Emergency Cesarean Delivery.
Gary S. Leiserowitz, MD, MS and Herman Hedriana, MD | November 30, 2021
View more articles from the same authors.
The Case
A 32-year-old pregnant woman presented to Labor and Delivery with prelabor rupture of membranes at 37 weeks’ gestation. She had significant obstetric history with 5 prior vaginal deliveries, all at term, with no attendant complications. The fetal heart rate (FHR) at presentation was category 2, described as moderate variability with normal baseline; accelerations were present with sporadic variable decelerations. On vaginal examination, her cervix was noted at 7 cm, right occiput transverse, -1 station, with adequate contractions coming every 3 minutes. Regional anesthesia was requested.
After dosing of regional anesthesia, the patient was placed in supine position with a leftward tilt. The FHR and uterine monitors were adjusted when suddenly FHR dropped to 60 beats per minute below baseline. Oxygen via face mask and position change were initiated, but the FHR remained depressed for 120 seconds without signs of returning to baseline. Upon vaginal examination, the obstetric provider diagnosed umbilical cord prolapse and called for an emergency cesarean delivery for fetal bradycardia. The infant was born 10 minutes after the cesarean was called with Apgar scores of 4 (1 minute) and 9 (5 minutes). Umbilical cord gases showed mixed acidosis with an arterial pH of 7.0 and base excess of -12. Uterine atony was noted after delivery of the placenta, which quickly responded to oxytocin bolus and uterine massage with a quantitated blood loss (QBL) of 1200 ml. The patient was hemodynamically stable when transferred to the post-anesthesia care unit (PACU) with intravenous fluid running at 125 ml/hour, and vital signs to be checked every 15 minutes, according to protocol.
Through the first 90 minutes in the PACU, the uterine fundus remained moderately firm. Vital signs showed systolic blood pressure around 90 mm Hg, mean arterial pressure (MAP) 60-70, pulse 110-120/min, respiratory rate 24-28/min. The patient was deemed stable. All monitor alarm functions were silenced to help the patient rest until a bed became available on the maternity floor. After 180 minutes in the PACU, the patient’s nurse discovered her unresponsive and the bedsheets were blood-soaked. The obstetrician and anesthesiologist were summoned and responded quickly. At that time, the patient’s vital signs showed a blood pressure of 88/40, mean arterial pressure of 57, pulse 142/min, respiratory rate 26/min, and 98% oxygen saturation. The intravenous fluid was opened up as a bolus. The uterus was boggy on examination. Uterotonic medications were ordered and administered. Quantitated blood loss was estimated at 1500 ml. A massive transfusion protocol was ordered. The patient remained hypotensive and tachycardic with continued vaginal bleeding, so the decision was made to return to the operating room for laparotomy and possible hysterectomy. Upon abdominal entry, the uterus was noted to be atonic despite uterotonic therapy. There was no other source of bleeding. Given the patient remained hemodynamically unstable, she underwent an emergency hysterectomy. As she continued to bleed after surgery, she had angiography and embolization of a small bleeding artery in the pelvis. She was transferred to the intensive care unit (ICU) and required intubation and mechanical ventilation for two days. She made a complete recovery without any sequelae.
The Commentary
By Gary S. Leiserowitz, MD, MS and Herman Hedriana, MD
Hundreds of women die in childbirth annually in the United States; 60% of these deaths are considered preventable.1 The U.S. has an increasing rate of maternal mortality of 17 maternal deaths per 100,000 and is ranked 60th in the world.2 The number of women with severe maternal morbidity greatly exceeds the number of deaths, increasing > 75% from the late 1990’s through 2009.2 Based on a review of 14 Maternal Mortality Review Committees (state and local) from 2008 – 2017, the causes of death in decreasing frequency were: cardiovascular conditions, hemorrhage, infection, embolism, cardiomyopathy, mental health conditions, and pre-eclampsia/eclampsia (cumulatively responsible for about 75% of deaths).3 In this review, the CDC estimated that 65.8% were preventable. The leading causes also varied by race. Cardiomyopathy and cardiovascular conditions were leading causes of death in non-Hispanic Black women, whereas mental health conditions were the leading cause in non-Hispanic White women. Hemorrhage was a cause of death in about 13% of all women. Mental health causes of maternal death appear related to drug overdose and suicide.4
The causes of maternal death have changed over time. The triad of infection, hemorrhage, and hypertensive disorders previously accounted for more than 90% of deaths, but now account for about one-third.4 With medical advances and use of assisted reproductive technology, many women are now able to become pregnant who would have been unable to so in prior decades. Also, many women are delaying childbearing until later in their reproductive life, which can increase the risk for co-morbid medical conditions, including cardiovascular, cerebrovascular, and other conditions. Increasing maternal age is strongly correlated with increased risk of maternal death; 27% of maternal deaths occur in women over age 35.5 This age-related mortality risk is especially pronounced for non-Hispanic Black women, who have 4 times higher mortality than white women of similar age. Low socioeconomic status, Medicaid insurance status, and obesity also increase the risk for severe obstetric morbidity.5,6
Creanga et al. used the Pregnancy Mortality Surveillance System with data from 2006 through 20105 and found that hemorrhage was the cause of death in 11.4% of cases, with the following specific etiologies: ruptured ectopic (3.0%), uterine rupture (1.1%), placental abruption (1.1%), placenta accreta (1.4%), uterine atony (1.8%), and other (2.8%). Other etiologies that manifest in abnormal vital signs and/or mental status changes included embolism (14.9%), hypertensive crisis (including pre-eclampsia/eclampsia, 9.4%), infection (13.6%), cardiomyopathy (11.8%), cerebrovascular accident (6.2%), and cardiovascular conditions (14.6%).
Given that more than 60% of maternal deaths are preventable the challenge for obstetric providers is to develop early detection systems that might trigger timely intervention. In this case, there was a potentially preventable etiology of severe maternal morbidity that might have been mitigated by earlier intervention. The physiology of pregnancy is substantially altered compared to the non-pregnant state to support the developing fetus and to provide a buffer in case complications arise. These alterations include increased blood volume, increased cardiac output, decreased vascular resistance (with lower blood pressure), increased pulse, increased respirations, increased minute ventilation, decreased functional residual capacity, increased glucose metabolism, and others.7 Altered pregnancy physiology can make it challenging to recognize any early clinical deterioration. Nevertheless, acknowledging these adjustments in maternal physiology allows for the recognition of abnormal triggers, which would inform an early warning system tailored to pregnant women.
Early Warning Systems
Multiple institutions and organizations have developed protocols and mechanisms to better identify pregnant patients who are at high risk of clinical decompensation in the peri-partum period. These protocols are evidence-based and customizable, aid in timely diagnosis and treatment, and can facilitate improvements in the quality of maternal care.2 They commonly use triggers associated with impending serious conditions. An example is a set of physiological changes along with alterations in cognition, known as maternal early warning criteria (MEWC), that was developed through the National Partnership for Maternal Safety.8 These criteria include systolic BP (mm Hg) <90 or >160, heart rate <50 or >120, oxygen saturation on room air <95%, and status changes such as agitation, confusion, dyspnea, and/or non-remitting headache. These criteria are somewhat analogous to what has been developed for medical admissions but modified based on maternal physiology.9 When triggered, this early warning system would lead to a targeted evaluation to determine the etiology, and if confirmed, should result in appropriate intervention. Since these triggers are based on objective physiological changes and/or cognition, they should be less influenced by the patient’s race/ethnicity, helping to minimize the risks of implicit bias. Several early maternal warning systems are available: modified early obstetric warning score (MOEWS), maternal early recognition criteria (MERC), modified early warning system (MEWS) and maternal early warning trigger (MEWT).10 All of these algorithms use pre-defined physiologic parameters as triggers, can be assessed at bedside, and are designed to lead to intervention. Some use a single abnormality to trigger a response (MERC),8 while others use a combined score (MEWS and MOEWS).11,12 MERC was proposed by the National Partnership for Maternal Safety as a system that favors simplicity and specificity over complexity and sensitivity.8 The authors note that in reviews of maternal mortality, a disproportionate share of patients showed clearly abnormal vital signs that should have triggered intervention. The MERC system’s simplicity makes it an attractive option for most obstetric units that have a normal spectrum of maternal conditions (as opposed to high acuity units characterized by complex maternal conditions). In contrast with other early maternal warning systems, MEWT requires that the physiologic abnormality(ies) be persistent over time (>20 minutes), to avoid a premature trigger of intervention.13 It was designed to address the four most common sources of maternal morbidity: sepsis, cardiopulmonary dysfunction, pre-eclampsia/hypertension, and hemorrhage. The MEWT algorithm includes both the defined triggers as well as the recommended interventions in a flow diagram that is readily available to obstetric providers and nurses. It was piloted in 6 hospitals in a regional system with 29 maternity facilities in California. Comparing the before and after implementation phases at the 6 pilot hospitals vs. the 23 others, implementation of the MEWT tool was followed by significant reductions in severe maternal morbidity at the pilot hospitals, with no concurrent trends at the non-pilot hospitals. The numbers of ICU admissions were unchanged. Hedriana and colleagues also did a retrospective case-control study of obstetric patients admitted to an ICU and analyzed the test performance of the MEWT system to identify maternal morbidity.14 They found that sustained MEWTs (2 or more, lasting 30 minutes or longer) had strong positive predictive value (72% detection rate) and a false-positive rate of 4%. For 62% of these patients, detailed chart review suggested that earlier intervention (e.g., bedside involvement of the provider or a rapid response team) might have changed the degree of maternal morbidity.
There are no head-to-head comparisons, but Blumenthal et al. did a retrospective analysis of severe maternal morbidity cases at an academic institution, applied the four maternal warning systems, and assessed their accuracy to distinguish between cases (79) and controls (123).10 The test performances of the four systems varied significantly; MEOWS and MERC were more sensitive (67.1% and 67.1%, respectively, versus 19% for MEWS and 40.5% for MEWT) but MEWS and MEWT were more specific (93.5% and 88.6%, respectively, versus 51.2% for MEOWS and 60.2% for MERC). Because the MEWT system requires a sustained positive trigger, it was less affected by transitory alarms. For example, single parameter alerts such as a diastolic blood pressure >90 mm Hg were common in the MEOWS system, accounting for 88% of alerts. None of the four tools performed with 90% sensitivity and 95% specificity. The authors noted that in an obstetric unit with high volume and acuity, the MEWT system performed the best with a positive predictive value of 70%, due to its higher specificity with fewer false positive alerts. MEWT was felt to provide clinically relevant information in 89% of alerts, compared to less than 50% for MEOWS and MERC.
Using this case as an example, the MEWT system would have been triggered, based on 2 maternal triggers (pulse >110 and respiratory rate >24) that were sustained for more than 30 minutes. (By comparison, MERC might not have triggered, because of its higher pulse threshold of 120 and respiratory rate threshold of 30.) The patient did not appear clinically ill, and so alarms were silenced without further evaluation. These triggers preceded the patient’s clinical deterioration, when she became non-responsive in the setting of hemorrhage and profound hypotension. Fortunately, the actual source of the patient’s condition was recognized in time, but heroic interventions were required. Although death was averted, it seems quite evident that earlier investigation and intervention might have led to less morbidity.
Systems Approach to Patient Safety
Two other issues should be highlighted. As noted at the beginning of the commentary, obstetric hemorrhage remains a major cause of maternal death, which is readily preventable. A previous WebM&M commentary notes that obstetric hemorrhage remains an under-recognized danger since it is often concealed from view (especially when intra-abdominal). Because of the increased blood volume and decreased vascular resistance seen late in pregnancy, blood loss can be profound prior to recognition, at which point the patient is severely compromised. The obstetric hemorrhage bundle described by the California Maternity Quality Care Collaborative (CMQCC) is an excellent example of standardized protocols that have proven to be effective interventions.15,16 A key element in the CMQCC response plan is a formalized checklist that is well organized, includes a list of likely etiologies, expedited evaluation, and action plans stratified by stages of severity – ranging from moderate to life-threatening. The intervention plans start on the left of the chart with “Mobilize”, then “Act” and on the right with “Think”. The strength of this checklist is that it clearly spells out a plan that avoids missing critical elements that are easy to overlook in an emergency. This checklist readily meets the standards described in the American College of Obstetricians and Gynecologists (ACOG) Committee Opinion on Clinical Guidelines and Standardization of Practice to Improve Outcomes by avoiding unnecessary variation in approach.15
The last issue is that frequent alerts can lead to alarm fatigue. Medical devices are a valuable adjunct for monitoring patient status since physiological triggers are non-auditory. There are three types of alarms generated by monitoring devices: arrhythmia alarms (change in cardiac rhythm), parameter violation alarms (vital signs that exceed “too low” or “too high” limits, and technical alarms (poor signal quality). Manufacturers set the sensitivity high to avoid the risk of missing a critical signal, but the specificity can be low, thus resulting in many false alarms. In one study of 77 ICU beds in a unit over a one-month period, there were 381,000 audible alarms with an average of 187 alarms per bed per day.17 There were 12,671 arrhythmia alarms of which 88.8% were false alarms. Desensitization to these incessant alarms has been linked to numerous patient deaths when critical information was ignored. The Joint Commission issued a Sentinel Event Alert in 201318 in response to a spate of deaths related to alarm-related sentinel events including alarm fatigue (most common), as well as equipment malfunctions/failures, inadequate training, inadequate staffing, and alarm settings that are not customized to the patients. The strategies to address these problems are multifold and include appropriate responses in high-risk areas, setting alarm limits relevant to the clinical situation, modifying and minimizing alarm use as appropriate, along with adequate training and staffing. Clearly, even though alarm fatigue related to the frequency of false alarms is expected, silencing an alarm is a dangerous response as was seen in this case. Alarms should never be silenced, but instead staff should investigate why an alarm persistently fires and then work to resolve it.
Take-Home Points
Maternal deaths and severe maternal morbidity remain major challenges in the United States, with rising incidence despite the increasing sophistication of maternity care. It is very problematic that race and ethnic disparities are reflected in maternal deaths and morbidity.
More than 65% of maternal deaths and morbidity are preventable.
Use of early maternal warning systems, when linked to standardized checklists and protocols, are key to avoiding poor maternal outcomes.
Obstetric hemorrhage is a major contributor to poor maternal outcomes but is readily managed when recognized early and is amenable to standardized interventions.
Alarm fatigue is a well-known result of frequent false alarms from monitoring devices. Adequate staffing and training are critically important in the management of monitoring devices. Alarms should never be silenced, but instead staff should investigate why an alarm persistently fires and then work to resolve it.
Gary S. Leiserowitz, MD, MS
Professor and Chair
Department of Obstetrics and Gynecology
UC Davis Heath
[email protected]
Herman Hedriana, MD
Professor and Director,
Division of Maternal-Fetal Medicine
Department of Obstetrics and Gynecology
UC Davis Health
[email protected]
Failure to Rescue the Mother
Melissa S. Wong, MD; Angelica Vivero, MD; Ellen B. Klapper, MD; and Kimberly D. Gregory, MD, MPH | July 2, 2019
View more articles from the same authors.
The Case
A 27-year-old woman, G5 P2 A2, was first admitted to the hospital at 25 weeks of pregnancy for vaginal bleeding. An ultrasound showed an anterior placenta, low lying and covering the cervix. She received 4 units of packed red blood cells and 2 doses of iron injections, and she was discharged after 3 days with an improved hemoglobin level.
The patient continued to have regular visits to her obstetrics clinic for monitoring. At 32 weeks, she was readmitted for vaginal bleeding, received 1 unit of packed red blood cells, and was then discharged. An ultrasound at 34 weeks revealed a central placenta previa covering the internal os and evidence of placenta percreta, with part of the placenta growing into the uterine wall. The scan results were discussed by the patient’s obstetrician, the obstetrics and gynecology department chief, and a urologist, and they recommended that the delivery be done by elective lower segment cesarean delivery with pos
Unformatted Attachment Preview
healthcare
Article
A Nationwide Study of the “July Effect” Concerning
Postpartum Hemorrhage and Its Risk Factors at Teaching
Hospitals across the United States
Zahra Shahin 1, * , Gulzar H. Shah 1 , Bettye A. Apenteng 1 , Kristie Waterfield 1
1
2
*
Citation: Shahin, Z.; Shah, G.H.;
Apenteng, B.A.; Waterfield, K.;
Samawi, H. A Nationwide Study of
the “July Effect” Concerning
Postpartum Hemorrhage and Its Risk
and Hani Samawi 2
Department of Health Policy and Community Health, Jiann-Ping Hsu College of Public Health,
Georgia Southern University, P.O. Box 8015, Statesboro, GA 30458, USA
Department of Biostatistics, Epidemiology and Environmental Health Sciences,
Jiann-Ping Hsu College of Public Health, Georgia Southern University, P.O. Box 8015,
Statesboro, GA 30458, USA
Correspondence: [email protected]
Abstract: Objective To assess the “July effect” and the risk of postpartum hemorrhage (PPH) and its
risk factors across the U.S. teaching hospitals. Method This study used the 2018 Nationwide Inpatient
Sample (NIS) and included 2,056,359 of 2,879,924 single live-birth hospitalizations with low-risk
pregnancies across the U.S. teaching hospitals. The International Classification of Diseases, Tenth
Revision (ICD-10) from the American Academy of Professional Coders (AAPC) medical coding was
used to identify PPH and other study variables. Multivariable logistic regression models were used
to compare the adjusted odds of PPH risk in the first and second quarters of the academic year vs.
the second half of the academic year. Results Postpartum hemorrhage occurred in approximately
4.19% of the sample. We observed an increase in the adjusted odds of PPH during July through
September (adjusted odds ratios (AOR), 1.05; confidence interval (CI), 1.02–1.10) and October through
December (AOR, 1.07; CI, 1.04–1.12) compared to the second half of the academic year (January to
June). Conclusions This study showed a significant “July effect” concerning PPH. However, given the
mixed results concerning maternal outcomes at the time of childbirth other than PPH, more research
is needed to investigate the “July effect” on the outcomes of the third stage of labor. This study’s
findings have important implications for patient safety interventions concerning MCH.
Keywords: postpartum hemorrhage; causes of postpartum hemorrhage; management; July effect;
patient safety
Factors at Teaching Hospitals across
the United States. Healthcare 2023, 11,
788. https://doi.org/10.3390/
healthcare11060788
Academic Editor: Simona Zaami
Received: 3 February 2023
Revised: 2 March 2023
Accepted: 3 March 2023
Published: 7 March 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Postpartum hemorrhage (PPH) is an obstetric emergency and one of the contributing
factors to maternal morbidity and mortality [1,2]. Researchers categorized four “T’s”,
including Tune (uterine atony), Tissue (retained tissue, invasive placenta), Trauma (uterine
rupture, genital tract laceration, and uterine inversion), and Thrombin (coagulation abnormalities, disseminated intravascular coagulation) as the causes of PPH [3]. A populationbased cohort study observed that prolonged labor, episiotomy, delayed initial care for
PPH, specifically administration of oxytocin more than 10–20 min after PPH diagnosis, and
waiting more than 10 min to call for additional assistance (an obstetrician and an anesthesiologist), increased the risk of severe PPH [4]. Studies also found that PPH increases with
the third stage duration of 20 min, and the risk of severe PPH rises with the third stage
duration of 23–25 min [5,6]. Every ten minutes of extra delay has been associated with an
increased adjusted odds of PPH risk of 1.11 (1.02–1.21) and 1.14 (1.03–1.27) for the risk of
severe PPH [7].
PPH has also been associated with poor obstetric practice and clinical management of
postpartum hemorrhage. Delayed diagnosis of severe hemorrhage, poor team communication, limited access to timely and quality care, delayed transfusion, and inadequate access
Healthcare 2023, 11, 788. https://doi.org/10.3390/healthcare11060788
https://www.mdpi.com/journal/healthcare
Healthcare 2023, 11, 788
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to resources such as blood products have been shown to be contributing factors that can
increase the risk of severe PPH [8–10].
Active management of the third stage of labor is critical in reducing PPH occurrence
but is influenced by many factors, including standard guidelines and protocols, quick
service accessibility, availability of resources (equipment and staff), and quality of care
services [9–12].
Researchers suggested that the arrival of new and inexperienced residents and interns
may increase adverse patient outcomes [13–15]. July is the month that the academic year
begins, experienced residents and fellows depart, and new residents and interns arrive [13].
Some studies observed increased adverse patient outcomes or inefficient care during this
transition, known as the “July effect” [13–15]. A previous study using 2018 Nationwide
Inpatient Sample (NIS) data to examine the association of the risk of PPH with hospital
characteristics found that teaching hospitals across the U.S. had the highest adjusted odds
of increased risks of PPH [16]. Therefore, the same data (the NIS 2018 data) was used to
assess the “July effect” on the increased risks of PPH across U.S. teaching hospitals. In
addition, the study investigated the “July effect” concerning factors that increase the risk of
PPH, such as failed induction of labor, puerperal infections, other obstetric trauma, and
perineal laceration. This study is the first to focus on the correlation between the “July
effect” and PPH risk across U.S. teaching hospitals.
2. Methods
This retrospective cohort study used the 2018 National Inpatient Sample (NIS) from
the Healthcare Cost and Utilization Project’s (HCUP) databases. NIS is a hospital inpatient
stay database derived from hospitals’ billing data from statewide data organizations across
the U.S. The NIS database includes all patient discharges from community hospitals in the
U.S.; however, since 2012, it has not included rehabilitation hospitals or long-term acute care
hospitals. The NIS data provides the opportunity to create national and regional estimates
by analyzing weighted data to produce accurate, unbiased results and demonstrate larger
universe data [17]. It includes clinical and resource use information from discharge abstracts
and contains data for all hospital stays, regardless of payer. The NIS uses the ICD-10CM/PCS coding system to report a full calendar year of data with diagnosis and procedure
codes. At the beginning of the 2016 data year, the NIS coding schema changed from the
ICD-9-CM diagnosis codes to the ICD-10-CM/PCS codes [18].
2.1. Study Population
A sample of women aged ≤19–54 in the third stage of labor with an index for postpartum hemorrhage (PPH) was selected using the NIS data from 1 January 2018 to 30 December
2018 across the U.S. teaching hospitals. This study included women with low-risk clinical conditions associated with maternal hemorrhage and excluded cases with previous
c-sections, women with intrapartum hemorrhage, placental abruption, placenta previa, and
high-risk cases in obstetrics because of their high-risk clinical conditions associated with
maternal hemorrhage.
2.2. Measures or Variables
The International Classification of Diseases, Tenth Revision (ICD-10) from the American Academy of Professional Coders (AAPC) for medical coding was used and identified
codes O61, O63, O70, O71, O72, and O86 for failed induction of labor, prolonged labor, perineal laceration during delivery, other obstetric trauma, postpartum hemorrhage
(PPH), and puerperal infections, respectively. Other codes, including O0993, O432, O43213,
O43223, O43233, O468X3, O458X3, O4693, O610, O611, O618, O619, O63, O630, O702, O703,
O710, O719, O8611, O8612, O8619, Z3800, and Z3801, were selected for further analysis
(Appendix A) [18,19].
The main dependent variable of interest for this study was PPH, and the corresponding
ICD-10 code was identified as O72. The independent variable included the “July effect”,
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which was operationalized by categorizing the variable month (month of hospitalization)
into three groups: (1) January to June (the second half of the academic year); (2) July to
September (the first quarter of the academic year); and (3) October to December (the second
quarter of the academic year).
Covariates included the assessed region of the teaching hospital location, maternal age,
race, or ethnicity, and variables that increase the risks of PPH, including failed induction of
labor, prolonged labor, puerperal infections, other obstetric trauma, and perineal laceration
during delivery. Maternal age was categorized as ≤19, 20–34, and 35–54 years of age.
Maternal race and ethnicity were defined based on HCUP coding as follows: (1) White,
(2) Black, (3) Hispanic, (4) Asian or Pacific Islander, (5) Native American, and (6) Other [18].
2.3. Statistical Analysis
We performed all analyses with STATA software version 16.1 [19]. Five multivariable
logistic regression models were used to estimate the adjusted odds ratio and 95% confidence
intervals (CI) of PPH and factors related to increasing PPH risk while controlling for regions
of teaching hospitals, race, and age. A sixth multivariable logistic regression was performed
to estimate the adjusted odds ratio and 95% confidence intervals (CI) of PPH in the first and
second quarters with the second half of the academic year while controlling for regions of
teaching hospitals, age, race, or ethnicity, plus PPH risk factors (including failed induction
of labor, other obstetric trauma, perineal lacerations (3rd and 4th degree), and puerperal
infections) [8,20].
This study was exempted by Georgia Southern University’s institutional review board
(IRB) from full board review as it uses de-identified secondary data.
3. Results
In 2018, there were 2,879,924 single live-birth hospitalizations with low-risk clinical
conditions associated with maternal hemorrhage among women ages ≤19–54 across the
U.S. teaching hospitals. Of these, 48.74% were performed in the months of January through
June, 26.27% from July through September, and 24.99% from October through December.
Approximately 4.2% of the women had postpartum hemorrhage (PPH). In the sample
population, women’s races included White 49.80%, Black 15.84%, Hispanic 21.81%, Asian
or Pacific Islander 6.96%, Native-American 0.54%, and other 5.05%. The age group of
≤19 accounted for approximately 5.19%, the age group of 20–34 accounted for almost
94.60%, and the age group of 35–54 accounted for only 0.21%.
3.1. Test of July Effect: Multivariable Logistic Regression of PPH and Its Risk Factors
We performed five multivariable logistic regressions to test the association of PPH
risk and each of its risk factors separately with the month of delivery (to indicate the “July
effect”) while holding regions of teaching hospitals, race, and age constant (see Table 1).
Results showed that compared to women who delivered during January through June,
those who delivered during July through September had significantly higher adjusted odds
of PPH (AOR, 1.05; CI, 1.02–1.10). Compared to deliveries from January through June, the
adjusted odds for PPH were also significantly higher during the months of October through
December (AOR, 1.07; CI, 1.04–1.12). However, there were no observed discharge month
effects for the assessed PPH risk factors—failed induction of labor, puerperal infections,
perineal laceration, and obstetric trauma.
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Table 1. Logistic regression of Postpartum Hemorrhage and Factors Increased PPH Risk across the U.S. Regions and Hospital Characteristics.
Variables
Month Reference (Jan–June)
July–September
October–December
Region Reference (Northeastern)
Midwest
South
West
RACE Reference (White)
Black
Hispanic
Asian or Pacific Islander
Native American
Other
AGE
Postpartum Hemorrhage
Failed Induction of Labor
Puerperal Infections
Perineal Laceration
Other Obstetric Trauma
AOR and CI
p-Value
AOR and CI
p-Value
AOR and CI
p-Value
AOR and CI
p-Value
AOR and CI
p-Value
1.05 (1.02–1.10)
1.07 (1.04–1.12)
0.004
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