Adventist: June 2017-June 2018 100 2500 90 80 2000 Percent of Total Transports (%) 70 Transport Volume 60 1500 50 40 1000 30 20 500 10 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Total Volume
Bakersfield Memorial: June 2017-June 2018 100 2000 90 1800 80 1600 Percent of Total Transports 70 1400 Transport Volume 60 1200 50 1000 40 800 30 600 20 400 10 200 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Transport Volume
Bakersfield Heart Hospital: June 2017-June 2018 100 400 90 350 80 300 Percent of Total Transports 70 Total Transport Volume 250 60 50 200 40 150 30 100 20 50 10 0 0 July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Transport Volume
Kern Medical: June 2017-June 2018 100 1400 90 1200 80 Percent of Total Transports (%) 1000 70 Transport Volume 60 800 50 600 40 30 400 20 200 10 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Transport Volume
Mercy: June 2017-June 2018 100 900 90 800 80 700 Percent of all Transports (%) 70 600 Transport Volume 60 500 50 400 40 300 30 200 20 100 10 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Total Volume
Mercy SW: June 2017-May 2018 100 900 90 800 80 700 Percent of Total Transports (%) 70 600 Transport Volume 60 500 50 400 40 300 30 200 20 100 10 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Total Volume
Delano: June 2017- June 2018 Delano APOT-2: June - December 2017 100 500 90 450 80 400 Percent of Total Transports (%) 70 350 Transport Volume 60 300 50 250 40 200 30 150 20 100 10 50 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5
Tehachapi: June 2017-June 2018 100 160 90 140 80 Percent of Total Transports (%) 120 70 Transport Volume 100 60 50 80 40 60 30 40 20 20 10 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Total Volume
KVHD: June 2017-June2018 100 200 90 180 80 160 Total Percent of Transports (%) 70 140 Transport Volume 60 120 50 100 40 80 30 60 20 40 10 20 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Transport Volume
Ridgecrest: June 2017-June 2018 100 350 90 300 80 Percent of Total Transports (%) 250 70 Number of Transports 60 200 50 150 40 30 100 20 50 10 0 0 June July Aug Sept Oct Nov Dec Jan Feb March April May June 2.1 2.2 2.3 2.4 2.5 Total Volume
APOT and Patient Outcomes Review of an Australian Study on patient outcomes and ambulance offload delays in emergency departments
Background • Ambulance offload delay is an emerging issue world-wide that is affecting care quality, patient safety, and resource availability for both EDs and ambulance providers • In the US, the national wait time doubled from 20 minutes to 45 minutes from 2006-2014 • Patient-level consequences have not been well studied, but it is hypothesized that offload delays could lead to delays in definitive care, poor pain control, increased morbidity, and increased mortality • Understanding when delays are most likely to occur may guide quality improvement efforts Source: https://emsa.ca.gov/wp-content/uploads/sites/47/2017/07/Toolkit-Reduce-Amb-Patient.pdf
Australian Study • Objective: to describe and compare characteristics and outcomes or patients who arrive by ambulance to ED • Compare patients with a delayed ambulance (<30) offload time with those who were not delayed
Methods • Retrospective study in 3 major public hospitals in Queensland, Australia • Patients • Ambulance users from September 2007-2008 • Linked data from 3 separate databases: ambulance, ED, and hospital discharge • Compared outcomes for ambulance offload times designated “delayed” or “non - delayed” • Delayed: >30 min
Results: Patient Demographics • Total Patient Population: 40,783 • 6,122 (15%) experienced offload delay • Patients who experienced offload delays compared to those who did not experience delays were: • Older • Transported during evening shift (between 15 and 22.59hr) • Transported on a Friday • Transported during winter months
Results: Patient Demographics (Hospital B) Characteristic Non-delayed Delayed P-value (non- n=12711 (74.1%) n=4444 (25.9%) delayed vs delayed) Median Age 42 (22-64) 52 (32-72) <0.001 Shift Presentation <0.001 Morning 38.7% 43.1% Evening 36.9% 46.1% Night 24.4% 10.8% Weekday/Weekend <0.001 Weekday 69.2% 77.1% Weekend 30.8% 22.9% Season <0.001 Summer 28.6% 23.4% Autumn 23.2% 24.1% Winter 21.2% 28.6% Spring 27.0% 23.9%
Results: Patient Outcomes • Overall, patients offloaded within 30 min had better outcomes for: • Time to triage • Ambulance time at ED • Time to see healthcare professional • Being seen within triage scale time frame • ED length of stay for both admitted and non-admitted patients • Admission rates (for 1 out of 3 hospitals) • Median hospital length of stay (for 1 out of 3 hospitals) • No statistically significant differences for in-hospital mortality rates
Results: Patient Outcomes (Hospital B) Outcome Non-delayed Delayed P-value (non- n=12711 (74.1%) n=4444 (25.9%) delayed vs delayed) Seen within Triage Scale Time 4758 (39.4%) 1034 (23.9%) <0.001 Frame (n, %) Median ED LOS (min) 265 357 <0.001 Admitted (n, %) 4121 (32.4%) 1651 (37.2%) <0.001 Median hospital LOS (days) 2 3 <0.001 In-hospital mortality, all 144 (3.5%) 56 (3.4%) 0.848 admits (n, %)
Conclusions • Off load delays affect how quickly patients can access medical care • Off load delays may also affect hospital functioning • Potential for more higher admission rates and longer hospital stays for those with longer delays • Off load delays may not have an affect on mortality rates for all admissions
Limitations • Hospital system evaluated in study may not be generalizable to Kern County System • Lack of comparisons within specific patient groups/diagnostic or triage categories
Kern County System January – June 2018
Queensland System Kern County System • Overall, 6,122 (15%) transports • Overall, 12,199 (30.8%) experienced delays transports experienced delays • Median age of delayed vs. non- • Median age of delayed vs. non- VS delayed: 52 vs. 42 delayed: 58 vs. 53 • Experienced highest percent of • Experienced highest percent of delays on weekdays delays on weekdays
The largest proportion of acute care visits arrive via ambulance during the weekdays. 600 Total Visits Ambulance Tranports 500 Number of acute care interactions 400 300 200 100 0 Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Despite Sunday being the busiest day for acute care visits, most offload delays occur Wednesday-Friday 35 Overall Delayed 30 25 Percent of Transports 20 15 10 5 0 Monday Tuesday Wednesday Thursday Friday Saturday Sunday
References • Crilly, J., Keijzers, G., Tippett, V., Odwyer, J., Lind, J., Bost, N., . . . Wallis, M. (2015). Improved outcomes for emergency department patients whose ambulance off-stretcher time is not delayed. Emergency Medicine Australasia,27 (3), 216-224. doi:10.1111/1742-6723.12399 • https://emsa.ca.gov/wp-content/uploads/sites/47/2017/07/Toolkit- Reduce-Amb-Patient.pdf
Opioid Overdose Epidemic National and local data
National Data Centers for Disease Control and Prevention
Drug Overdose Death Data o Opioids were involved in 42,249 deaths in 2016 o 5X higher than 1999 o States with the highest opioid-related death rates in 2016: o West Virginia, Ohio, New Hampshire, Pennsylvania, and Kentucky o CA did not experience a statistically significant increase in opioid- related deaths between 2010 and 2016
Source: https://www.cdc.gov/drugoverdose/data/statedeaths.html
Synthetic Opioids o Synthetic opioids (Fentanyl and Fentanyl analogs) were the most common type of opioid involved in overdose deaths for 2015-2016 o Geographically clustered east of the Mississippi River o Fentanyl powder is more readily mixed with white powder heroin than black tar heroin o Becoming increasingly available with non-opioid drugs o Benzodiazepines, counterfeit opioid pills, ketamine, cocaine, and methamphetamine Source: https://content.govdelivery.com/accounts/USCDC/bulletins/1fdd9bf
What does this mean to EMS? o CA recorded 373 fentanyl overdose deaths in 2017 (19% of all opioid overdose deaths) o Suggests fentanyl /fentanyl analogs are not a huge issue yet o CDC recommends the following: o Use extreme caution when handling unknown substances and white powders o If fentanyl is suspected, multiple doses of naloxone may be needed to properly treat patient
Opioid Overdose Data: Kern County California Opioid Dashboard e PCR data
N=659 *Rate is per 100,000 population based on 2017 estimates
According to EMS call data, male les are more likely to overdose than females. 252, 38% Female 407, 62% Male
The highest rate of EMS calls for opioid overdose is in the 25 25-29 29 year old age group. 180 160 140 Rate (per 100,000) 120 100 80 60 40 20 0 *Rates calculated from population based on 2017 estimates
Although we see a higher rate of calls in 25 25-29 yea ear old olds, 55 55-59 year old olds account have higher rates of hospital discharges for opioid-related issues 400.0 350.0 300.0 Rate (per 100,000) 250.0 200.0 150.0 100.0 50.0 0.0 Data from 2016
According to *state data (2017), the 50 50-55 55 age group experiences the highest rate of opioid-related deaths 30 25 20 Rate (per 100K) 15 10 5 0 *Estimated crude death rate for 2017https://discovery.cdph.ca.gov/CDIC/ODdash/
Differences in *Estimated Crude Death Rates, by Opiate and Age Group (N=71) Prescription Heroin 85+ 0 0 85+ 80-84 80-84 0 9.41 75-79 75-79 0 0 70-74 70-74 0 4.11 65-69 65-69 8.97 0 60-64 11.64 60-64 4.66 55-59 55-59 19.76 5.93 50-54 50-54 7.96 11.94 45-49 45-49 5.86 0 40-44 40-44 9.39 1.88 35-39 35-39 3.38 1.69 0 30-34 30-34 4.67 25-29 1.33 25-29 7.98 20-24 7.28 20-24 2.91 15-19 1.52 15-19 0 10-14 0 10-14 0 0 5-9 5-9 0 0 <5 <5 0 20 15 10 5 0 0 5 10 15 20 Rate (per 100K) Rate (per 100K) *Data provided by: https://discovery.cdph.ca.gov/CDIC/ODdash/
For More Information about Kern County and California Opioid Overdose Deaths: • https://discovery.cdph.ca.gov/CDIC/ODdash/ • State and County Dashboards • Note the “Technical Notes” while interpreting data comparing Kern County to California
Narcan Use and Opioid Overdose EMS Data
Percent of Calls where Narcan was used, by Age Group 90 80 70 Percent (by age group) 60 50 40 30 20 10 0
Patient Status after Administration of Narcan, by Age Group (N=331) 60 Unchanged Improved 50 Number of Patients 40 30 20 10 0 Age Group
Individual Zip Code Data Where are overdoses occurring?
Where are Overdoses Occurring? • Investigated zip codes with highest number or rate of EMS calls for overdose • Did the overdose occur in a private residence vs public area? • Based on incident address and zip code • Google mapped the incident address • Public area • Parks, businesses, intersections, hotels • Private residence • If address of a house or apartment was listed
Location of Incident, by Zip Code 100% 90% 80% Percent of Calls (by zip code) 70% 60% 50% 40% 30% 20% 10% 0% 93268 93308 93307 Tehachapi Area 93306 Home Public Correctional Facility Other
Questions? Comments?
Sources • https://www.cdc.gov/drugoverdose/data/statedeaths.html • https://www.cdc.gov/drugoverdose/epidemic/index.html • https://content.govdelivery.com/accounts/USCDC/bulletins/1fdd9bf • https://discovery.cdph.ca.gov/CDIC/ODdash/
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