Medicaid-Insured Client Characteristics and Quit Outcomes at the Arizona Smokers' Helpline

Leveling the Playing Field: Mailing Pharmacotherapy to Medicaid Members Who Smoke

INTRODUCTION

Smoking rates differ by insurance type; rates are often double for Medicaid and uninsured compared with that for Medicare or privately insured. State-funded tobacco quitlines' provision of free nicotine replacement therapy varies. In some states, Medicaid beneficiaries must obtain nicotine replacement therapy from a physician, whereas others get nicotine replacement therapy mailed to them.

METHODS

This secondary analysis examined the differences in the source and use of cessation treatment by insurance type and their impacts on cessation. The parent trial excluded people who were pregnant, had private insurance, or were not ready to quit. From June 1, 2017 to November 15, 2020, a total of 1,944 low-income people who smoke daily completed a baseline survey and were enrolled in a quitline program; 1,380 (71%) completed a 3-month follow-up. Analyses were completed in August 2022. Participants were classified as Medicaid/dual (55%), Medicare/Veterans Affairs (14%), or uninsured (31%). Nine months into the trial, owing to a system error, the quitline provided nicotine replacement therapy to all study participants regardless of insurance type.

RESULTS

Before error versus after error, Medicaid participants reported lower nicotine replacement therapy receipt (3.2% vs 50.8%) and use (32.4% vs 52.6%). The odds of quitting (7-day point prevalence) by 3 months increased for people who smoke who completed more quitline calls and used any (36% quit) versus used no (20% quit) pharmacotherapy, but quitting did not differ by insurance classifications (27%-29%). Getting and using nicotine replacement therapy from the quitline produced the highest quit rates (38%).

CONCLUSIONS

Results illustrate the benefit of receiving nicotine replacement therapy from the quitline on cessation. Mailing nicotine replacement therapy to all people who smoke should be standard practice to reduce smoking disparities.

Preoperative Risk Management Programs at the Top 50 Orthopaedic Institutions Frequently Enforce Strict Cutoffs for BMI and Hemoglobin A1c Which May Limit Access to Total Joint Arthroplasty and Provide Limited Resources for Smoking Cessation and Dental Care

BACKGROUND

Performing elective orthopaedic surgery on patients with high BMI, poorly controlled hyperglycemia, and who use tobacco can lead to serious complications. Some surgeons use cutoffs for BMI, hemoglobin A1c, and cigarette smoking to limit surgery to patients with lower risk profiles rather than engaging in shared decision-making with patients about those factors. Other studies have suggested this practice may discriminate against people of lower income levels and women. However, the extent to which this practice approach is used by orthopaedic surgeons at leading hospitals is unknown.

QUESTIONS/PURPOSES

(1) How often are preoperative cutoffs for hemoglobin A1c and BMI used at the top US orthopaedic institutions? (2) What services are available at the top orthopaedic institutions for weight loss, smoking cessation, and dental care? (3) What proportion of hospital-provided weight loss clinics, smoking cessation programs, and dental care clinics accept Medicaid insurance?

METHODS

To investigate preoperative cutoffs for hemoglobin A1c and BMI and patient access to nonorthopaedic specialists at the top orthopaedic hospitals in the United States, we collected data on the top 50 orthopaedic hospitals in the United States as ranked by the 2020 US News and World Report 's "Best Hospitals for Orthopedics" list. We used a surgeon-targeted email survey to ascertain information regarding the use of preoperative cutoffs for hemoglobin A1c and BMI and availability and insurance acceptance policies of weight loss and dental clinics. Surgeons were informed that the survey was designed to assess how their institution manages preoperative risk management. The survey was sent to one practicing arthroplasty surgeon, the chair of the arthroplasty service, or department chair, whenever possible, at the top 50 orthopaedic institutions. Reminder emails were sent periodically to encourage participation from nonresponding institutions. We received survey responses from 70% (35 of 50) of hospitals regarding the use of preoperative hemoglobin A1c and BMI cutoffs. There was no difference in the response rate based on hospital ranking or hospital region. Fewer responses were received regarding the availability and Medicaid acceptance of weight loss and dental clinics. We used a "secret shopper" methodology (defined as when a researcher calls a facility pretending to be a patient seeking care) to gather information from hospitals directly. The use of deception in this study was approved by our institution's institutional review board. We called the main telephone line at each institution and spoke with the telephone operator at each hospital asking standardized questions regarding the availability of medical or surgical weight loss clinics, smoking cessation programs, and dental clinics. When possible, researchers were referred directly to the relevant departments and asked phone receptionists if the clinic accepted Medicaid. We were able to contact every hospital using the main telephone number. Our first research question was answered using solely the surgeon survey responses. Our second and third research questions were addressed using a combination of the responses to the surgeon surveys and specific hospital telephone calls.

RESULTS

Preoperative hemoglobin A1c cutoffs were used at 77% (27 of 35) of responding institutions and preoperative BMI cutoffs were used at 54% (19 of 35) of responding institutions. In the secret shopper portion of our study, we found that almost all the institutions (98% [49 of 50]) had a medical weight loss clinic, surgical weight loss clinic, or combined program. Regarding smoking cessation, 52% (26 of 50) referred patients to a specific department in their institution and 18% (9 of 50) referred to a state-run smoking cessation hotline. Thirty percent (15 of 50) did not offer any internal resource or external referral for smoking cessation. Regarding dental care, 48% (24 of 50) of institutions had a dental clinic that performed presurgical check-ups and 46% (23 of 50) did not offer any internal resource or external referral for dental care. In the secret shopper portion of our study, for institutions that had internal resources, we found that 86% (42 of 49) of weight loss clinics, 88% (23 of 26) of smoking cessation programs, and 58% (14 of 24) of dental clinics accepted Medicaid insurance.

CONCLUSION

Proceeding with TJA may not be the best option for all patients; however, surgeons and patients should come to this consensus together after a thoughtful discussion of the risks and benefits for that particular patient. Future research should focus on how shared decision-making may influence patient satisfaction and a patient's ability to meet preoperative goals related to weight loss, glycemic control, smoking cessation, and dental care. Decision analyses or time trade-off analyses could be implemented in these studies to assess patients' tolerance for risk.

CLINICAL RELEVANCE

Orthopaedic surgeons should engage in shared decision-making with patients to develop realistic goals for weight loss, glycemic control, smoking cessation, and dental care that consider patient access to these services as well as the difficulties patients experience in losing weight, controlling blood glucose, and stopping smoking.

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.