Background: Opioid abuse continues to be an ongoing issue in the United
States. Prescriber prescriptions play a large role in this epidemic. This study
describes opioid prescribing patterns following cesarean section before and after
the New York State Department of Health (NYSDOH) mandated the Opioid Prescriber
Training Program in 2017. Methods: This is a retrospective cohort study
of 1494 women hospitalized for cesarean section at a single institution in New
York City between July 2016 and August 2018. We obtained patient data via chart
review. Results: Primary outcome was total amount of opioid prescribed
before and after the Opioid Prescriber Training. Secondary outcomes included
outpatient opioid prescription habits by provider level, as well as outpatient
opioid prescription patterns related to the amount of inpatient opioid use; and
patient, surgical, and hospital-specific factors. There was a significant
difference in opioids prescribed before and after training. The median dose of
opioid prescribed pre and post intervention was 150 morphine milligram
equivalents (MME) which is equal to 20 pills of 5 mg of oxycodone. Pre-training,
41.1% of prescriptions amounted to
Opioid abuse in the United States has heightened to dangerous proportions in the past 15 years. According to the Centers for Disease Control and Prevention (CDC), the opioid-related death toll in the United States (US) increased five-fold from 1999 to 2016 [1]. As a result, in 2017 the US Department of Human and Health Services called this “Opioid Crisis” a state of public emergency, and subsequently is funding research and issuing strategies to combat this ongoing problem.
A major contributing factor to this epidemic is the increase in opioid prescriptions. Per the CDC, from 1999 through 2014 opioid prescription sales increased four-fold, and of the 42,000-plus opioid related deaths in 2016, 40% were attributed to a prescription opioid [1]. This rise in opioid prescriptions increases opioid availability as well as potential for abuse and misuse. A 2019 national survey in the US quoted over 10 million people taking prescription opioids for nonmedical reasons, commonly using opioids not prescribed to them or using their prescription for other indications than were initially prescribed [2]. In addition, there is a correlation between prescription opioid use and future heroin abuse [3].
In response to the opioid crisis in New York State, the New York State Department of Health (NYSDOH) mandated that all providers who prescribe opioids, including resident physicians under a faculty Drug Enforcement Administration (DEA), complete a three-hour online course entitled “Opioid Prescriber Training Program” by July 1, 2017 [4]. This course describes the various options for pain management in the outpatient and palliative care setting, the physiology of opioids, evidence regarding opioid overuse and consequences of addiction. Most states have specific required coursework, lasting 1–3 hours, for opioid prescribers in this vane. However, no other state mandates resident physicians prescribing opioids to complete a mandatory online training course.
Cesarean section is the most common surgical procedure undergone by reproductive aged women. According to the CDC, there were 1,258,581 Cesarean Deliveries in the US in 2016 [1]. At our institution, Montefiore Medical Center, Weiler Campus there are approximately 4000 deliveries per year and our Cesarean Delivery rate is about 30%. According to a nationwide survey, 85% of women receive a prescription for opioids after their cesarean section [5]. At our institution, Montefiore Medical Center, all patients undergoing cesarean section receive a prescription for opioids upon discharge unless there is a medical contraindication and/or patient declines. Though most women receive an opioid prescription after cesarean section, only a small percentage of opioid naïve women (1 in 300) become persistent opioid users [6]. The trend in new persistent opioid use over the last decade has decreased [7]. Despite this low incidence, the large number of cesarean sections performed annually translates into a significant number of newly dependent patients and hence plays a role in this serious health problem.
Data regarding opioid prescriptions after cesarean section has revealed that for the most part, the amount of opioids prescribed surpasses the amount consumed [8, 9]. One prospective survey study showed only half the amount of prescribed opioid tablets was reportedly consumed by patients [8]. However much of this data is prior to 2017, and there is an inadequate amount of knowledge on obstetrician prescriber habits after the NYSDOH mandated that all prescribers take an opioid course.
Our primary objective was to describe opioid prescriber practices at time of hospital discharge following cesarean section in a large cohort of women before and after the NYSDOH mandated the Opioid Prescriber Training Program. Secondary aims were to analyze opioid prescription habits by provider level as well as to identify trends in opioid prescription patterns related to the amount of inpatient opioid use as well as surgical and/or hospital specific factors.
This is a retrospective cohort analysis of women at a single high-volume academic institution who underwent a cesarean section and were discharged from the hospital. This study included all 1494 women hospitalized for cesarean delivery from July 1, 2016 to December 31, 2016 and then from January 1, 2018 to August 31, 2018, excluding women within the one year surrounding the deadline for the NYSDOH mandated Opioid Prescriber Training Program (January 1, 2017 to December 31, 2017). This tertiary center is in an urban area with a multilingual, racially, and ethnically diverse population. Over 80 attending physicians, resident physicians, and physician assistants employed by the hospital system treated these patients and were mandated to complete the online training course.
At our institution, women routinely receive neuraxial anesthesia (epidural and/or spinal) for cesarean delivery. In the post-operative period, the obstetrical team manages pain, and women generally receive multi-modal pain management including oral short-acting opioids, most commonly oxycodone or Percocet (oxycodone-acetaminophen). Discharge medications are prescribed at the individual provider’s discretion. There are no current guidelines at our hospital for inpatient or outpatient opioid prescriptions. Though there is a post-Cesarean order set in our electronic medical record system Epic (Epic Systems Corporation, Verona, WI, USA), there is no discharge navigator or discharge order set that includes prescriptions.
The Institutional Review Board at Montefiore Medical Center, Albert Einstein College of Medicine approved this study on July 15th, 2019. We obtained information via chart review within EPIC. Our center’s electronic medical record system includes all outpatient and inpatient records. We queried demographic information including age, race, ethnicity, and primary language. We then obtained all clinical and pharmacologic data, including patient and surgery specific characteristic as well as inpatient medications and outpatient prescriptions, directly from the electronic medical record. The data was transferred to an electronic database and double checked independently by two members of the research team. If there was missing data, it was labeled as “unknown”. We used the same electronic record to see if an opioid was prescribed at discharge, and if so, we included information on the type, strength and number of pills prescribed. We converted all opioids into total morphine milligram equivalents (MME) using conversion rates from CDC.gov to compare amounts more effectively among the different opioids [10]. We obtained this value by converting each opioid dosage to MME and then multiplying by the number of pills [11]. The literature frequently uses this MME conversion to compare amounts between opioids. Our primary outcome was total MME prescribed for outpatient use. We analyzed the total outpatient MME prescribed at discharge before and after the mandated NYSDOH Opioid Prescriber Training. Secondary outcomes included analyzing outpatient opioid prescription habits by provider level. As well as identifying trends in outpatient opioid prescription patterns related to inpatient opioid use, patient (i.e., body mass index, age, race), surgical factors (surgery length, indication for cesarean, estimated blood loss, skin incision, skin closure, anesthesia) and hospital factors (length of stay, infection). Since this study was not focused on actual patient use, we did not collect information on whether the prescriptions were filled.
We computed descriptive statistics (frequencies, medians, and interquartile
ranges) to summarize patient, surgical and hospital-specific factors pre and post
intervention, as well as across all patients. We assessed the association between
cohort (pre vs. post-intervention) and each factor via chi-square test. Due to
the clustering of the data with relation to dosing, the data could not be
analyzed as a continuous variable, therefore the decision was made to use MME
categories. In-house opioid use was categorized as
Our study included 1494 patients who received a cesarean section at Montefiore
Medical Center Weiler Campus between July 1, 2016 to December 31, 2016 and
January 1, 2018 to August 31, 2018 and were discharged from the hospital. Of
these patients, 739 had their cesarean between July 1, 2016 through December 31,
2016 before the mandated NYDOH opioid training (the “pre-intervention” group),
and 755 had their cesarean between January 1, 2018 through August 31, 2018 after
the mandated opioid training (the “post-intervention” group). The patient
demographic and obstetric characteristics were similar between groups (Table 1a).
The majority of patients were less than 35 years old, Hispanic, English-speaking
and nulliparous. Most patients were overweight or obese (BMI
Characteristics | Pre-intervention | Post-intervention | Overall (%) | p-value | |
Age (years) | |||||
Missing | 100 (13.5%) | 97 (12.8%) | 197 (13.2%) | 0.386 | |
166 (22.5%) | 149 (19.7%) | 315 (21.1%) | |||
26–30 | 168 (22.7%) | 170 (22.5%) | 338 (22.6%) | ||
31–35 | 169 (22.9%) | 193 (25.6%) | 362 (24.2%) | ||
36–40 | 96 (13%) | 103 (13.6%) | 199 (13.3%) | ||
41–45 | 39 (5.3%) | 37 (4.9%) | 76 (5.1%) | ||
46–50 | 1 (0.1%) | 4 (0.5%) | 5 (0.3%) | ||
0 (0%) | 2 (0.3%) | 2 (0.1%) | |||
Race | |||||
Asian | 53 (7.2%) | 57 (7.5%) | 110 (7.4%) | 0.010 | |
White | 82 (11.1%) | 46 (6.1%) | 128 (8.6%) | ||
Black | 203 (27.5%) | 221 (29.3%) | 424 (28.4%) | ||
Unknown | 311 (42.1%) | 348 (46.1%) | 659 (44.1%) | ||
Other | 90 (12.2%) | 83 (11%) | 173 (11.6%) | ||
Ethnicity | |||||
Spanish/Hispanic/Latino | 371 (50.2%) | 370 (49%) | 741 (49.6%) | 0.110 | |
Not Spanish/Hispanic/Latino | 308 (41.7%) | 342 (45.3%) | 650 (43.5%) | ||
Unknown | 60 (8.1%) | 43 (5.7%) | 103 (6.9%) | ||
English preferred language | |||||
Yes | 603 (81.6%) | 594 (78.7%) | 1197 (80.1%) | 0.157 | |
No | 136 (18.4%) | 161 (21.3%) | 297 (19.9%) | ||
Gravida | |||||
1 | 157 (21.2%) | 189 (25%) | 346 (23.2%) | 0.010 | |
2 | 189 (25.6%) | 162 (21.5%) | 351 (23.5%) | ||
3 | 155 (21%) | 158 (20.9%) | 313 (21%) | ||
4 | 119 (16.1%) | 91 (12.1%) | 210 (14.1%) | ||
119 (16.1%) | 155 (20.5%) | 274 (18.3%) | |||
Parity | |||||
0 | 266 (36%) | 287 (38%) | 553 (37%) | 0.290 | |
1 | 249 (33.7%) | 226 (29.9%) | 475 (31.8%) | ||
2 | 152 (20.6%) | 147 (19.5%) | 299 (20%) | ||
3 | 50 (6.8%) | 59 (7.8%) | 109 (7.3%) | ||
4 | 12 (1.6%) | 22 (2.9%) | 34 (2.3%) | ||
10 (1.4%) | 14 (1.9%) | 24 (1.6%) | |||
Prior CS | |||||
0 | 382 (51.7%) | 384 (50.9%) | 766 (51.3%) | 0.826 | |
1 | 223 (30.2%) | 226 (29.9%) | 449 (30.1%) | ||
2 | 106 (14.3%) | 109 (14.4%) | 215 (14.4%) | ||
28 (3.8%) | 36 (4.8%) | 64 (4.3%) | |||
BMI (unit kg/m |
|||||
Unknown | 154 (20.8%) | 106 (14%) | 260 (17.4%) | 0.013 | |
2 (0.3%) | 1 (0.1%) | 3 (0.2%) | |||
18.5–24.9 | 39 (5.3%) | 29 (3.8%) | 68 (4.6%) | ||
25–29.9 | 142 (19.2%) | 167 (22.1%) | 309 (20.7%) | ||
30–34.9 | 174 (23.5%) | 185 (24.5%) | 359 (24%) | ||
35–39.9 | 114 (15.4%) | 139 (18.4%) | 253 (16.9%) | ||
114 (15.4%) | 128 (17%) | 242 (16.2%) |
The patients’ hospital and surgical characteristics were similar between groups. Table 1b shows the relevant details about each cesarean section and hospital course stratified by whether the patients underwent the surgery before or after the mandated opioid training. The majority of patients in both groups had unscheduled cesarean sections (pre-intervention: 67.3%, post-intervention: 62.8%; p-value = 0.07). The most common indication for cesarean section was repeat procedure followed by fetal intolerance, and labor dystocia. In majority of the cases the estimated blood loss was less than 1000 cc. However, there was a statistical significance between cohorts with 71% of cases pre-intervention reporting a blood loss less than 1000 cc versus 60.7% post-intervention (p = 0.001). In most cases sutures were used for skin closure, but the percentage of surgeries utilizing staples significantly decreased from 9.3% pre-intervention to 5.4% post-intervention (p-value = 0.009). Surgical time varied significantly as well. The majority of cases in the pre-intervention group lasted between 30 to 60 minutes versus between 60 to 90 minutes in the post-intervention group (p-value = 0.005). For both groups, the most common length of stay in the hospital was 2–3 days. Many of the patients in both groups had horizontal skin incisions (97%) and neuraxial anesthesia (97–98%). Most had no other procedures performed at the time of cesarean (86–88%), or in-hospital infections (89–90%). Finally, there was a statistically significant association between the level of provider prescribing the opioids and cohort (pre vs post intervention) (p-value = 0.001). However, within both the pre and post intervention groups, the largest percentage of opioid prescribers were first year resident physicians (pre-intervention: 66.2%, post-intervention: 72.5%).
Characteristics | Pre-intervention | Post-intervention | Overall (%) | p-value | |
C-section scheduled | |||||
Scheduled | 242 (32.7%) | 281 (37.2%) | 523 (35%) | 0.070 | |
Unscheduled | 497 (67.3%) | 474 (62.8%) | 971 (65%) | ||
Estimated blood loss (mL) | |||||
525 (71%) | 458 (60.7%) | 983 (65.8%) | 0.001 | ||
214 (29%) | 297 (39.3%) | 511 (34.2%) | |||
Indication | |||||
Elective Repeat | 293 (39.6%) | 305 (40.4%) | 598 (40%) | 0.078 | |
Elective Primary* | 31 (4.2%) | 34 (4.5%) | 65 (4.4%) | ||
Fetal Malpresentation | 55 (7.4%) | 74 (9.8%) | 129 (8.6%) | ||
Active Herpes | 3 (0.4%) | 4 (0.5%) | 7 (0.5%) | ||
Suspected Fetal Macrosomia | 5 (0.7%) | 6 (0.8%) | 11 (0.7%) | ||
Fetal Intolerance of Labor | 223 (30.2%) | 175 (23.2%) | 398 (26.6%) | ||
Labor Dystocia | 98 (13.3%) | 127 (16.8%) | 225 (15.1%) | ||
Other† | 31 (4.2%) | 30 (4%) | 61 (4.1%) | ||
Surgery time (minutes) | |||||
Missing | 23 (3.1%) | 28 (3.7%) | 51 (3.4%) | 0.005 | |
9 (1.2%) | 17 (2.3%) | 26 (1.7%) | |||
30–60 | 354 (47.9%) | 288 (38.1%) | 642 (43%) | ||
60–90 | 267 (36.1%) | 302 (40%) | 569 (38.1%) | ||
90–120 | 67 (9.1%) | 89 (11.8%) | 156 (10.4%) | ||
120–150 | 12 (1.6%) | 18 (2.4%) | 30 (2%) | ||
150–180 | 4 (0.5%) | 4 (0.5%) | 8 (0.5%) | ||
3 (0.4%) | 9 (1.2%) | 12 (0.8%) | |||
Length of hospital stay (days) | |||||
2–3 | 325 (44%) | 334 (44.2%) | 659 (44.1%) | 0.657 | |
4 | 203 (27.5%) | 218 (28.9%) | 421 (28.2%) | ||
5 | 101 (13.7%) | 107 (14.2%) | 208 (13.9%) | ||
110 (14.9%) | 96 (12.7%) | 206 (13.8%) | |||
In-house infection | |||||
None | 664 (89.9%) | 672 (89%) | 1336 (89.4%) | 0.596 | |
Infections‡ | 75 (10.1%) | 83 (11%) | 158 (10.6%) | ||
Skin incision | |||||
Horizontal (Pfannenstiel/Mallard) | 715 (96.7%) | 738 (97.7%) | 1453 (97.3%) | 0.298 | |
Midline Vertical | 24 (3.2%) | 17 (2.3%) | 41 (2.7%) | ||
Skin closure | |||||
Suture | 673 (90.7%) | 717 (94.6%) | 1390 (92.7%) | 0.009 | |
Staples | 69 (9.3%) | 41 (5.4%) | 110 (7.3%) | ||
General anesthesia | |||||
No | 718 (97.2%) | 737 (97.6%) | 1455 (97.4%) | 0.527 | |
Yes | 21 (2.8%) | 18 (2.4%) | 39 (2.6%) | ||
Other procedures | |||||
None | 637 (86.2%) | 665 (88.1%) | 1302 (87.1%) | 0.621 | |
Bilateral Tubal Ligation | 83 (11.2%) | 77 (10.2%) | 160 (10.7%) | ||
Cesarean Hysterectomy | 6 (0.8%) | 6 (0.8%) | 12 (0.8%) | ||
Myomectomy | 5 (0.7%) | 2 (0.3%) | 7 (0.5%) | ||
Other | 8 (1.1%) | 5 (0.7%) | 13 (0.9%) | ||
Provider level | |||||
No prescription given | 35 (4.7%) | 43 (5.7%) | 78 (5.2%) | 0.001 | |
Postgraduate Year 1 (PGY1) | 489 (66.2%) | 547 (72.5%) | 1036 (69.3%) | ||
Postgraduate Year 2 (PGY2) | 82 (11.1%) | 95 (12.6%) | 177 (11.8%) | ||
Postgraduate Year 3 (PGY3) | 39 (5.3%) | 20 (2.6%) | 59 (3.9%) | ||
Postgraduate Year 4 (PGY4) | 10 (1.4%) | 9 (1.2%) | 19 (1.3%) | ||
Physician Assistant | 10 (1.4%) | 11 (1.5%) | 21 (1.4%) | ||
Fellow | 2 (0.3%) | 0 (0%) | 2 (0.1%) | ||
Attending | 72 (9.7%) | 30 (4%) | 102 (6.8%) | ||
*Elective Primary: includes maternal request for elective cesarean, maternal request for cesarean in the setting of twins and maternal request in the setting of a history of fourth degree laceration or shoulder dystocia. †Other: includes cesarean for placenta abruption, placenta previa, placenta accreta spectrum and history of myomectomy. ‡Infections: includes chorioamnionitis and endometritis cases. |
Table 2 summarizes the prescribing patterns pre and post training. A lower
percentage of prescriptions post- training were
Total narcotic (MME) | Number of patients pre-intervention (%) | Number of patients post-intervention (%) | Total patients (%) | Range of narcotics prescribed (MME) |
None | 35 (4.7%) | 42 (5.6%) | 77 (5.2%) | 0 |
101 (13.7%) | 170 (22.5%) | 271 (18.1%) | 15–140 | |
150 | 307 (41.5%) | 382 (50.6%) | 689 (46.1%) | 150 |
296 (45.1%) | 161 (21.3%) | 457 (30.6%) | 157.5–1800 | |
p-value None: includes patients with no prescriptions given. |
Provider levels | Prescribing patterns pre-intervention (%) | Prescribing patterns post-intervention (%) | ||||
150 MME | 150 MME | |||||
Postgraduate Year 1 | 71 (14.5%) | 254 (52%) | 164 (33.5%) | 128 (23.5%) | 296 (54.2%) | 122 (22.3%) |
Postgraduate Year 2 | 2 (2.4%) | 11 (13.4%) | 69 (84.2%) | 21 (22.1%) | 53 (55.8%) | 21 (22.1%) |
Postgraduate Years 3–4 | 6 (12.2%) | 11 (22.5%) | 69 (65.3%) | 8 (27.6%) | 13 (44.8%) | 8 (27.1%) |
Physician Assistants, Fellows, Faculty | 22 (26.2%) | 31 (36.9%) | 31 (36.9%) | 13 (31.7%) | 20 (48.8%) | 8 (19.5%) |
p-value (pre-intervention |
Factor | Group | Pre-intervention | Post-intervention |
OR (95% CI) | OR (95% CI) | ||
Age* | Ordinal | 0.98 (0.87, 1.11) | 1.05 (0.94, 1.19) |
Race | Asian | 0.89 (0.48, 1.66) | 1.03 (0.49, 2.15) |
Black | 1.16 (0.73, 1.85) | 0.96 (0.52, 1.76) | |
Unknown | 0.99 (0.63, 1.54) | 0.92 (0.51, 1.66) | |
Other | 1.14 (0.66, 1.99) | 0.86 (0.43, 1.72) | |
Ref: white | |||
Ethnicity | Spanish/Hispanic/Latino | 0.93 (0.7, 1.23) | 0.92 (0.69, 1.21) |
Unknown | 1.19 (0.72, 1.98) | 0.94 (0.51, 1.73) | |
Ref: NOT S/H/L | |||
Prior CS | Ordinal | 0.92 (0.78, 1.08) | 0.96 (0.82, 1.12) |
BMI* | Ordinal | 1.01 (0.95, 1.08) | 1.05 (0.98, 1.13) |
CS | Unscheduled | 0.85 (0.64, 1.13) | 1.21 (0.92, 1.6) |
Ref: Scheduled | |||
Surgery time* | Ordinal | 1.08 (0.92, 1.28) | 1.1 (0.95, 1.27) |
In-house infection | Infections |
0.98 (0.62, 1.54) | 0.97 (0.63, 1.51) |
Ref: none | |||
Skin Incision | Midline Vertical | 1.12 (0.71, 1.77) | 1.08 (0.63, 1.87) |
Ref: Horizontal | |||
Skin closure | Suture | 0.86 (0.39, 1.91) | 1.09 (0.6, 1.97) |
Ref: Staples | |||
General anesthesia | Yes | 1.84 (0.77, 4.39) | 0.88 (0.36, 2.16) |
Ref: No | |||
In-house narcotics prescribed | 50–100 MME | 1.10 (0.73, 1.66) | 1.74 (1.13, 2.68) |
1.28 (0.88, 1.89) | 1.58 (1.08, 2.3) | ||
Ref: |
|||
Other procedures performed | Yes | 1.46 (0.97, 2.19) | 0.73 (0.29, 1.83) |
Ref: No | |||
Prescribing provider level |
Ordinal | 1.15 (1, 1.32) | 0.94 (0.79, 1.12) |
Resident physicians | 1.63 (1.04, 2.54) | 1.38 (0.75, 2.54) | |
Ref: PA, fellow, faculty | |||
*Categories for Age: S/H/L: Spanish/Hispanic/Latino. |
Our study suggests that this training course may have assisted in altering prescriber practices after January 1, 2018, at our institution. Post-intervention, we found that higher amounts of opioids were prescribed less often, and lower amounts of opioids were prescribed more often; prescriptions of moderate amounts of opioids remained unchanged. Additionally, though not statistically significant on the multivariate model, there was an association noted between inhouse opioid use, and the amount of opioids prescribed. Our data also suggests that amount of opioid prescribed did not correlate with patient demographics, and surgical and hospital factors.
The median amount of prescribed opioid was 150 MME (20 pills of 5mg oxycodone)
both pre- and post-intervention. Though 150 MME remained the median amount of
opioid prescribed, we observed a decline in the percentage of prescriptions
There was a significant difference in BMI and race between the groups. We hypothesized that this was because more than 40% of the patients in the cohorts were documented as race “unknown”. Therefore, it is unclear which racial groups were most affected by this category and makes it difficult to determine if the difference noted between the groups is truly significant. We are not aware of any data showing changes in the racial distribution of patients at our institution during the time period studied. Our post intervention group had a decrease in patients with BMI less than 25 and an increase in the overweight and obese categories. The BMI changes observed in the post-intervention group are consistent with the increase in obesity reported in adults living in the Bronx from 2015 to 2019 and therefore are not surprising [12]. Between the two groups there was a significant difference in surgery time, estimated blood loss and skin closure. Surgeries were longer post-intervention possibly due to an increase in new, young hires who frequently can have longer surgical times when compared to experienced faculty. The difference in estimated blood loss between groups may be explained by the implementation of “Quantitative Blood Loss” measurements where nurses calculated blood loss for deliveries using a standard formula. Though we used “Estimated Blood Loss” in our study, we understand the measurement of quantitative blood loss may bias providers’ estimations as well. The majority of cases were closed using sutures. Though the decrease in the use of staples post-intervention was significant, we do not believe that this is clinically relevant, and would have to perform future studies to determine a true association.
The main strength of our study is that we reviewed an extensive cohort of women in the pre, and post intervention groups and we were able to look at prescriber patterns for a large number of diverse providers of varying levels using a reliable EMR system.
This is a retrospective study with a high risk, urban patient population, which may not be generalizable of all cesarean deliveries in other states or countries. Our study only looked at our providers’ prescription habits, and this may not accurately depict which prescriptions were filled by the patients and/or amount of opioid consumed. There were a variable number of providers in each level, with the largest number of prescriptions being written by resident physicians. Our total analysis did not account for prescription habits between providers of different levels. However, we do not feel that controlling for provider level would alter the effect since in general PGY1–2 in the pre-intervention group were PGY3–4 in the post-intervention group.
Another limitation is that our institution briefly piloted an Enhanced Recovery After Surgery or ERAS protocol around the same time-period that we performed our chart review. This trial included 58 patients and used an inpatient only post-operative order set. Data from the protocol demonstrated that our ERAS pilot was not associated with a reduction in postoperative opioid use compared to standard of care [13]. There was an association between in-house opioid use and prescription patterns post-intervention with the univariate analysis and since ERAS was aimed to specifically decrease opioid use post-operatively there is the possibility it impacted our outcome.
Our study suggests a role in provider training and education in altering prescribing practices as a means of addressing the opioid epidemic. The positive shift in prescribing patterns in resident physicians supports that they be included in interventions to improve hospital practices and patient care. Opioid education should be included as part of the residency curriculum. This is especially significant since resident physicians will move on to become prescribing physicians themselves.
It is important to note that the observed changes in provider practices may not be solely related to this single intervention, but could be a combination of effects including the intervention as well as an overall increase in awareness through the media, news etc. Further studies would be needed to confirm causality.
There was an association between in house opioid use and prescribing patterns in the post-intervention group. Surprisingly, this association failed to be significant in the multivariate analysis. One would expect the amount of opioid use inhouse to predict use at home in the immediate post-operative setting, and therefore would be influential. More studies are needed to better elucidate this data. Patient demographic, surgical or hospital factors did not influence opioid prescriptions. Next steps would include a prospective study looking at the percentage of patients that filled their opioid prescriptions and actual patient consumption upon discharge.
TB was the primary investigator on this project. This author was involved in initial conceptualization and project planning. She supervised the initiation and progress of the entire project. She played a major role in writing final paper and was responsible for final review and editing. AC was involved in conceptualization of project with primary investigator. This author carried out the data research and recording in database. She played a large role in writing the original draft and in subsequent revisions. MF was the statistician on the project. This author provided formal statistical analysis and interpretation and wrote the statistical portions of the paper. GN assisted in data collection and input into the database. This author collaborated in writing the original draft. All authors read and approved the final manuscript.
The study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the Institutional Review Board at Montefiore Medical Center, Albert Einstein College of Medicine on July 15th, 2019. This was a chart review without direct patient contact, hence individual patient consent was not required. Institutional review board number is 2018-9111.
We would like to express gratitude to the department of Obstetrics and Gynecology at Montefiore Medical Center for their support and guidance during this project.
This research received no external funding.
The authors declare no conflict of interest.