The cost-to-charge ratio reflects the markup of hospital services. A lower cost-to-charge ratio indicates lower costs and/or greater charges. This study examines factors associated with ...cost-to-charge ratio trends to determine whether decreasing cost-to-charge ratio is associated with worse surgical outcomes.
The Florida Agency for Healthcare Administration Inpatient database (2018–2020) was queried for common surgical procedures and linked to the Distressed Communities Index, RAND Corporation Hospital data, Center for Medicare Services Cost Reports, and American Hospital Association data. Only hospitals with monotonically increasing or decreasing cost-to-charge ratio were included in the study. Univariable analysis compared these hospitals. Using patient-level data, interpretable machine learning predicted cost-to-charge ratio trend while identifying influential factors.
The cohort had 67 hospitals (27 increasing cost-to-charge ratio and 40 decreasing cost-to-charge ratio) with 35,661 surgeries. Decreasing cost-to-charge ratio hospitals were more often proprietarily owned (78% vs 33%, P = .01) and had greater mean total charges ($134,349 ± $114,510 vs $77,185 ± $82,027, P < .01) with marginally greater mean estimated costs ($14,863 ± $12,343 vs $14,458 ± $15,440, P < .01). Patients from decreasing cost-to-charge ratio hospitals had greater rates of most comorbidities (P < .05) but no difference in mortality or overall complications. Machine-learning models revealed charges rather than clinical factors as most influential in cost-to-charge ratio trend prediction.
Decreasing cost-to-charge ratio hospitals charge vastly more despite minimally greater estimated costs and no difference in outcomes. Although differences in case-mix existed, charges were the predominant differentiators. Patient clinical factors had far less of an impact.
By combining patient data with additional factors such as social determinants of health, hospital characteristics, and physician profiles, researchers can better understand the macro environment of ...healthcare delivery and the relative contribution of each factor. ...we arrive at predictive modeling using ML. ...data is the driving force in predictive analytics, ML, and Big Data.
This study characterizes the relationship between SES and cost of emergency general surgery (EGS).
Utilizing Florida AHCA (2016–2020), patients undergoing the 7 most common EGS were identified. ...Distressed Community Index (DCI) was linked, which quantifies SES through unemployment, poverty, and other factors. Zipcodes are assigned DCI 0 (no distress) to 100 (severe distress). Linear regression with stepwise elimination was conducted. Top and bottom DCI quintiles were propensity matched for demographics, comorbidities, and procedure.
144,924 admissions were included. Linear regression eliminated 5 of 28 variables, including DCI. Top cost contributors were discharge-43%; comorbidities-14%; age-9%. Distressed patients received less home health and inpatient rehab. Distressed patients utilized 4-/5-star hospitals less and had higher odds of mortality.
Discharge, mortality, and hospital characteristics differ significantly between DCI communities. Total cost was similar, and is strongly influenced by discharge status, while DCI had no effect.
•Emergency general surgery cost influenced by discharge status; SES had no effect.•Distressed community patients are less frequently admitted to 4-/5-star hospitals.•Distressed community patients had higher odds of mortality.•Distressed patients are less likely to receive home health care or inpatient rehab.•Top seven EGS procedures utilized to evaluate cost and clinical outcomes.
Background
COVID-19 caused healthcare systems to significantly alter processes of care. Literature on the pandemic’s effect on healthcare processes and resulting surgical outcomes is lacking. This ...study aims to determine outcomes of open colectomy in patients with perforated diverticulitis during the pandemic.
Methods
Using CDC data, the highest and lowest COVID mortality rates were calculated and used to establish 9-month COVID-heavy (CH) and COVID-light (CL) timeframes, respectively. Nine-months of 2019 were assigned as pre-COVID (PC) control. Florida AHCA database was utilized for patient-level data. Primary outcomes were length of stay (LOS), morbidity, and in-hospital mortality. Stepwise regression with 10-fold cross-validation determined factors most impacting outcomes. A parallel analysis excluding COVID-positive patients was performed to differentiate COVID-infection from processes of care.
Results
There were 3862 patients in total. COVID-positive patients had longer LOS, more intensive care unit admissions, and higher morbidity and mortality. After excluding 105 COVID-positive patients, individual outcomes were not different per timeframe. Regression showed timeframe did not affect primary outcomes.
Discussion
Outcomes following colectomy for perforated diverticulitis were worse for COVID-positive patients. Despite increased stress on the healthcare system during the pandemic, major outcomes were unchanged for COVID-negative patients. Our results indicate that despite COVID-associated changes in processes of care, acute care surgery can still be performed in COVID-negative patients without increased mortality and minimal change in morbidity.
Previous studies on nonoperative management (NOM) of acute appendicitis (AA) indicated comparable outcomes to surgery, but the effect of COVID-19 infection on appendicitis outcomes remains unknown. ...Thus, we evaluate appendicitis outcomes during the COVID-19 pandemic to determine the effect of COVID-19 infection status and treatment modality. We hypothesized that active COVID-19 patients would have worse outcomes than COVID-negative patients, but that outcomes would not differ between recovered COVID-19 and COVID-negative patients. Moreover, we hypothesized that outcomes would not differ between nonoperative and operative management groups, regardless of COVID-19 status.
We queried the National COVID Cohort Collaborative from 2020 to 2023 to identify adults with AA who underwent operative or NOM. COVID-19 status was denoted as follows: COVID-negative, COVID-active, or COVID-recovered. Intention to treat was used for NOM. Propensity score-balanced analysis was performed to compare outcomes within COVID groups, as well as within treatment modalities.
A total of 37,868 patients were included: 34,866 COVID-negative, 2,540 COVID-active, and 460 COVID-recovered. COVID-active and recovered less often underwent operative management. Unadjusted, there was no difference in mortality between COVID groups for operative management. There was no difference in rate of failure of NOM between COVID groups. Adjusted analysis indicated, compared with operative, NOM carried higher odds of mortality and readmission for COVID-negative and COVID-active patients.
This study demonstrates higher odds of mortality among NOM of appendicitis and near equivalent outcomes for operative management regardless of COVID-19 status. We conclude that NOM of appendicitis is associated with worse outcomes for COVID-active and COVID-negative patients. In addition, we conclude that a positive COVID test or recent COVID-19 illness alone should not preclude a patient from appendectomy for AA. Surgeon clinical judgment of a patient's physiology and surgical risk should, of course, inform the decision to proceed to the operating room.
Therapeutic/Care Management; Level III.
Firearm-related death rates continue to rise in the US. As some states enact more permissive firearm laws, we sought to assess the relationship between a change to permitless open carry (PLOC) and ...subsequent firearm-related death rates, a currently understudied topic.
Using state-level data from 2013 to 2021, we performed a linear panel analysis using a state fixed-effects model. We examined total firearm-related death, suicide, and homicide rates separately. If a significant association between OC law and death rate was found, we then performed a difference-in-difference (DID) analysis to assess for a causal relationship between changing to PLOC and increased death rate. For significant DID results, we performed confirmatory DID separating firearm and nonfirearm death rates.
Nineteen states maintained a no OC or permit-required law, whereas 5 changed to permitless and 26 had a PLOC before 2013. The fixed-effects model indicated more permissive OC law that was associated with increased total firearm-related deaths and suicides. In DID, changing law to PLOC had a significant average treatment effect on the treated of 1.57 (95% CI 1.05 to 2.09) for total suicide rate but no significant average treatment effect for the total firearm-related death rate. Confirmatory DID results found a significant average treatment effect on the treated of 1.18 (95% CI 0.90 to 1.46) for firearm suicide rate.
OC law is associated with total firearm-related death and suicide rates. Based on our DID results, changing to PLOC is indeed strongly associated with increased suicides by firearm.
Patients with low socioeconomic status (SES) are disadvantaged in terms of access to health care. A novel metric for SES is the Distressed Communities Index (DCI). This study evaluates the effect of ...DCI on hospital choice and distance traveled for surgery.BACKGROUNDPatients with low socioeconomic status (SES) are disadvantaged in terms of access to health care. A novel metric for SES is the Distressed Communities Index (DCI). This study evaluates the effect of DCI on hospital choice and distance traveled for surgery.A Florida database was queried for patients with symptomatic cholelithiasis or chronic cholecystitis who underwent an outpatient cholecystectomy between 2016 and 2019. Patients' DCI was compared with hospital ratings, comorbidities, Charlson Comorbidity Index, and distance traveled for surgery. Stepwise logistic regression was used to determine which factors most influenced distance traveled for surgery.METHODSA Florida database was queried for patients with symptomatic cholelithiasis or chronic cholecystitis who underwent an outpatient cholecystectomy between 2016 and 2019. Patients' DCI was compared with hospital ratings, comorbidities, Charlson Comorbidity Index, and distance traveled for surgery. Stepwise logistic regression was used to determine which factors most influenced distance traveled for surgery.There were 54,649 cases-81 open, 52,488 laparoscopic, and 2,080 robotic. There was no difference between surgical approach and patient's DCI group (p = 0.12). Rural patients traveled the farthest for surgery (avg 21.29 miles); urban patients traveled the least (avg 5.84 miles). Patients from distressed areas more often had surgery at one- or two-star hospitals than prosperous patients (61% vs 36.3%). Regression indicated distressed or at-risk areas predicted further travel for rural/small-town patients, while higher hospital ratings predicted further travel for suburban/urban patients.RESULTSThere were 54,649 cases-81 open, 52,488 laparoscopic, and 2,080 robotic. There was no difference between surgical approach and patient's DCI group (p = 0.12). Rural patients traveled the farthest for surgery (avg 21.29 miles); urban patients traveled the least (avg 5.84 miles). Patients from distressed areas more often had surgery at one- or two-star hospitals than prosperous patients (61% vs 36.3%). Regression indicated distressed or at-risk areas predicted further travel for rural/small-town patients, while higher hospital ratings predicted further travel for suburban/urban patients.Compared to prosperous areas, patients from distressed areas have surgery at lower-rated hospitals, travel further if they live in rural/small-town areas, but travel less if they live in suburban areas. We postulate that farther travel in rural areas may be explained by a lack of health care resources in poor, rural areas, while traveling less in suburban areas may be explained by personal lack of resources for patients with low SES.DISCUSSIONCompared to prosperous areas, patients from distressed areas have surgery at lower-rated hospitals, travel further if they live in rural/small-town areas, but travel less if they live in suburban areas. We postulate that farther travel in rural areas may be explained by a lack of health care resources in poor, rural areas, while traveling less in suburban areas may be explained by personal lack of resources for patients with low SES.
