On the data to know the prioritization and vulnerability of patients on surgical waiting lists.
ABSTRACT: The data presented in this article are complementary material to our work entitled "A decision support system for prioritization of patients on surgical waiting lists: A biopsychosocial approach". We prepared, together with physicians, a survey was used in the otorhinolaryngology unit of the Hospital of Talca for a period of five months, between February 05, 2018 and June 29, 2018. Two hundred and five surveys were collected through 20 biopsychosocial criteria, which allowed measuring the priority and vulnerability of patients on the surgical waiting list. The data allow choosing and preparing patients for surgery according to both a dynamic score and a vulnerability level.
Project description:OBJECTIVES:To investigate the national distribution of prolonged waiting for elective day case and inpatient surgery, and to examine associations of prolonged waiting with markers of NHS capacity, activity in the independent sector, and need. SETTING:NHS hospital trusts in England. POPULATION:People waiting for elective treatment in the specialties of general surgery; ear, nose and throat surgery; ophthalmic surgery; and trauma and orthopaedic surgery. MAIN OUTCOME MEASURE:Numbers of people waiting six months or longer (prolonged waiting). Characteristics of trusts with large numbers waiting six months or longer were examined by using logistic regression. RESULTS:The distribution of numbers of people waiting for day case or elective surgery in all the specialties examined was highly positively skewed. Between 52% and 83% of patients waiting longer than six months in the specialties studied were found in one quarter of trusts, which in turn contributed 23-45% of the national throughput specific to the specialty. In general, there was little evidence to show that capacity (measured by numbers of operating theatres, dedicated day case theatres, available beds, and bed occupancy rate) or independent sector activity were associated with prolonged waiting, although exceptions were noted for individual specialties. There was consistent evidence showing an increase in prolonged waiting, with increased numbers of anaesthetists across all specialties and with increased bed occupancy rates for ear, nose and throat surgery. Markers of greater need for health care, such as deprivation score and rate of limiting long term illness, were inversely associated with prolonged waiting. CONCLUSION:In most instances, substantial numbers of patients waiting unacceptably long periods for elective surgery were limited to a small number of hospitals. Little and inconsistent support was found for associations of prolonged waiting with markers of capacity, independent sector activity, or need in the surgical specialties examined.
Project description:<h4>Importance</h4>Adenotonsillectomy (ATE) is one of the most common surgical procedures to treat children with obstructive sleep apnea (OSA), but to our knowledge there are no randomized clinical trials confirming the benefit of surgery compared with watchful waiting in children between 2 and 4 years of age.<h4>Objective</h4>To determine whether ATE is more effective than watchful waiting for treating otherwise healthy children with mild to moderate OSA.<h4>Design, setting, and participants</h4>This randomized clinical trial was conducted from December 2014 to December 2017 at the Otorhinolaryngology Department of the Karolinska University Hospital, Stockholm, Sweden. A total of 60 children, 2 to 4 years of age, with an obstructive apnea-hypopnea index (OAHI) score of 2 or greater and less than 10, were randomized to ATE (n = 29) or watchful waiting (n = 31). A total of 53 participants (88%; ATE, n = 25; watchful waiting, n = 28) completed the study. Data were analyzed from August 2018 to December 2018.<h4>Interventions</h4>Adenotonsillectomy.<h4>Main outcomes and measures</h4>The primary outcome was the difference between the groups in mean OAHI score change. Secondary outcomes were other polysomnography parameters, score on the Obstructive Sleep Apnea-18 (OSA-18) questionnaire, and subgroup analyses. Polysomnography and the OSA-18 questionnaire were completed at baseline and after 6 months.<h4>Results</h4>Of the 60 included children, 34 (57%) were boys and the mean (SD) age at first polysomnography was 38 (9) months. Both groups had a decrease in mean OAHI score, and the difference in mean OAHI score change between the groups was small (-1.0; 95% CI, -2.4 to 0.5), in favor of ATE. However, there were large differences between the groups in favor of ATE regarding the OSA-18 questionnaire (eg, total OSA-18 score: -17; 95% CI, -24 to -10). Also, a subgroup analysis of 24 children with moderate OSA (OAHI ≥5 and <10) showed a meaningful difference in mean OAHI score change between the groups in favor of ATE (-3.1; 95% CI, -5.7 to -0.5). Of 28 children, 10 (36%) in the watchful waiting group received ATE after the follow-up, and 7 of these had moderate OSA at baseline.<h4>Conclusions and relevance</h4>This randomized clinical trial found only small differences between the groups regarding changes in OAHI, but further studies are needed. However, there were large improvements in quality of life after ATE. These results suggest that otherwise healthy children with mild OSA and mild effect on quality of life may benefit from watchful waiting, while children with moderate OSA should be considered for ATE.<h4>Trial registration</h4>ClinicalTrials.gov Identifier: NCT02315911.
Project description:<h4>Background</h4>Policies assigning low-priority patients treatment delays for care, in order to make room for patients of higher priority arriving later, are common in secondary healthcare services today. Alternatively, each new patient could be granted the first available appointment. We aimed to investigate whether prioritisation can be part of the reason why waiting times for care are often long, and to describe how departments can improve their waiting situation by changing away from prioritisation.<h4>Methods</h4>We used patient flow data from 2015 at the Department of Otorhinolaryngology, Haukeland University Hospital, Norway. In Dynaplan Smia, Dynaplan AS, dynamic simulations were used to compare how waiting time, size and shape of the waiting list, and capacity utilisation developed with and without prioritisation. Simulations were started from the actual waiting list at the beginning of 2015, and from an empty waiting list (simulating a new department with no initial patient backlog).<h4>Results</h4>From an empty waiting list and with capacity equal to demand, waiting times were built 7 times longer when prioritising than when not. Prioritisation also led to poor resource utilisation and short-lived effects of extra capacity. Departments where prioritisation is causing long waits can improve their situation by temporarily bringing capacity above demand and introducing "first come, first served" instead of prioritisation.<h4>Conclusion</h4>A poor appointment allocation policy can build long waiting times, even when capacity is sufficient to meet demand. By bringing waiting times down and going away from prioritisation, the waiting list size and average waiting times at the studied department could be maintained almost 90% below the current level - without requiring permanent change in the capacity/demand ratio.
Project description:<h4>Purpose</h4>To identify factors influencing patient's availability to re-schedule primary total knee replacement (TKR) or revision (RKR) surgery after the lockdown (March-May 2020) during the COVID-19 pandemic.<h4>Methods</h4>A prospective cohort study through a telephone survey was performed in 156 patients (143 for primary and 13 for revision) included in the TKR and RKR surgical waiting list before March 2020. Contact of each patient with COVID-19, stress and anxiety, perceived pain, and function were obtained in the interviews, and also the preference of each patient to have re-scheduled surgery (early or late). Finally, we registered their response (acceptance or refusal) when surgery was effectively re-scheduled.<h4>Results</h4>88 out of 156 patients waiting for knee replacement (76/143 of those waiting for TKR, 12/13 of those waiting for RKR) declared themselves ready for surgery in less than 1 month. When re-scheduled, 115 patients underwent surgery and 41 refused. Significantly different preferences were found for age (more prone to surgery if under 65), revision surgery (more readily available), pain (7.9 ± 1.7/10 in NRS in those undergoing surgery, 5.6 ± 2.3/10 in those refusing, p = 0.000), or COVID-19 diagnosis, but not other close contact with COVID-19, comorbidities, stress, or anxiety. A logistic regression model confirmed that revision surgery (OR 9.33), perceived severe pain (OR 5.21), and age under 65 years (OR 5.82) were significantly associated with patient preference. The probability of patients over 65 to prefer early surgery reached 60% only with pain at or above 9/10.<h4>Conclusions</h4>Surgical timing preferences for knee replacement vary between patients older than 65 years (immediate surgery only when pain is intense) and younger patients (immediate surgery no matter the amount of pain). Even if COVID-19 severely stroke our population, the need for knee replacement stood in the young population and even in the aged population at risk for COVID when pain was important.
Project description:To assess the impact of pre-operative breast MRI on surgical waiting time, and to identify factors contributing to the delay.A retrospective cohort study involving 1274 patients was conducted after obtaining institutional ethics review. Surgical candidates for newly diagnosed breast cancer from 2007 to 2013 at a tertiary center were divided into 2 groups: those who had pre-operative MRI and those who did not. Linear regression using matched populations was used to compare the surgical waiting times, defined as time from the date of the first positive biopsy to the date of surgery. Potential influences on surgical waiting time and subgroup analysis were obtained using median regression analysis and the Kruskal-Wallis test.Mean surgical waiting time was 57.9 days (95% CI: 55.6-60.1) for MRI patients, compared to 46.8 days (95% CI: 45.1-48.9) for the control group, after matching for potential confounding factors (p<0.0001). Increased surgical waiting time was associated with more favorable pathology, later year of diagnosis, older patient age, surgeon and summer time. Second-look ultrasound and subsequent biopsies were associated with increased waiting time (p = 0.001).Pre-operative breast MRI increased surgical waiting time by 11 days using a conventional average of differences, and by 12 days after using a full matching statistical method (p<0.0001), with the main contributor being additional post-MRI procedures and imaging.
Project description:Backround There is a shortage of medical specialists in Chile, including neurologists; currently, there are 56,614 patients waiting for a first adult Neurology appointment in the country. The Teleneurology Program at the Hospital Las Higueras de Talcahuano (HHT) was implemented in 2015 to help reduce both the number of patients waiting for a first consultation and their waiting times. METHODS:This retrospective study analyzed a cohort of 8269 patients referred to the HHT Neurology clinic between 2013 and 2018, from primary, secondary, and tertiary health centers. Cox regression analyses were performed to determine the factors influencing each outcome (number of patients waiting for a consultation and waiting time), such as age, gender, referral health establishment and the type of consultation (whether in situ at the HHT or by synchronic telepresence through the Teleneurology Program). RESULTS:Out of the 8269 patients included in the study, 1743 consulted the neurologist through the Teleneurology Program, while 6526 received a consultation in situ at the HHT. Since its implementation (2015) until the end of 2018, the Teleneurology program contributed to decrease the number of patients waiting for their first appointment from 3084 to 298. Waiting time for the first consultation was 60% shorter for patients enrolled in the Teleneurology program than those with consultation in situ at HHT (6.23?±?6.82 and 10.47?±?8.70?months, respectively). Similar differences were observed when sorting patient data according to the referral health center. Cox regression analysis showed that patients waiting for a traditional in situ first adult Neurology consultation at the HHT had a higher risk (OR?=?6.74) of waiting 90% longer than patients enrolled in the Teleneurology Program, without significant differences due to gender or age. CONCLUSIONS:Data from this study show a significant contribution of the Teleneurology Program at the HHT to decrease the number of patients waiting for a first consultation with a neurologist, as well as shorter waiting times, when derived from primary and secondary health centers.
Project description:In the absence of a price mechanism, emergency department waiting times act as a rationing device to equate demand for treatment with available supply. Sustained increases to demand stemming from population growth, aging populations, and rising comorbidities has caused waiting times internationally to rise. This has resulted in increased calls for higher funding from governments and commitments from both state and national governments to address excessive waiting times. This paper aims to determine the effectiveness of government funding for improving the median waiting times for treatment and the proportion of patients seen within clinically recommended waiting times. For this purpose, an econometric analysis was conducted on a panel of data on Victorian local health networks over the period 2015-2018. This is supplemented with a discussion of the alternative measures which governments might take to both address demand for emergency treatment, and also ensure that waiting time reductions can be maintained over the long-term.
Project description:BACKGROUND:Waiting times for elective treatments, including elective surgery, are a source of public concern and therefore are on policy makers' agenda. The long waiting times have often been tackled through the allocation of additional resources, in an attempt to reduce them, but results are not straightforward. At the same time, researchers have reported wide geographical variations in the provision of elective care not driven by patient needs or preferences but by other factors. The paper analyses the relationship between waiting times and treatment rates for nine high-volume elective surgical procedures in order to support decision making regarding the availability of these services for the citizens. Using the framework already proposed for the diagnostic services, we identify different patterns that can be followed to align the supply with patient needs in the Italian context. METHODS:After measuring the waiting times and the treatment rates for nine procedures in the 34 districts in Tuscany, we performed correlation analyses. Then, we plotted the results in a matrix cross-checking waiting times and rates. By doing so, we identified four different contexts that require a second step analysis to tackle unwarranted geographical variations and ensure timely care to patients. Finally, for each district and elective surgical procedure, we measured the economic impact of the different treatment rates in order to evaluate whether there are any supply criticalities and eventually some room for maneuver. We also included active and passive mobility of patients. RESULTS:The results show a high degree of variation both in treatment rates and waiting times, especially for the orthopaedic procedures: knee replacement, knee arthroscopy and hip replacement. The analysis performed for the nine interventions shows that the 34 districts are in varying positions in the waiting time-treatment rate matrix, suggesting that there is no straightforward relationship between rates and waiting times. Each combination in the matrix may have different determinants that require healthcare managers to adopt diversified strategies. The decision making process needs to be supported by a two-level analysis: the first one to put in place the matrix that cross-checks waiting times and treatment rates, the second one to analyse the characteristics of each quadrant and the improvement actions that can be proposed. CONCLUSIONS:In Italy, waiting times in elective surgical services are a main policy issue with a relevant geographical variation. Our analysis reveals that this variation is due to multiple elements. In order to avoid simplistic approaches that do not solve the problem but often lead to increased expenditure, policy makers and healthcare managers should follow a two-step strategy firstly identifying the type of context and secondly analysing the impact of elements such as resource productivity, resource availability, patients' preferences and care appropriateness. Only in some cases it is required to increase the service supply.
Project description:<h4>Background</h4>Prioritisation instruments were developed for patients on waiting list for hip and knee arthroplasties (AI) and cataract surgery (CI). The aim of the study was to assess their convergent and discriminant validity and inter-observer reliability.<h4>Methods</h4>Multicentre validation study which included orthopaedic surgeons and ophthalmologists from 10 hospitals. Participating doctors were asked to include all eligible patients placed in the waiting list for the procedures under study during the medical visit. Doctors assessed patients' priority through a visual analogue scale (VAS) and administered the prioritisation instrument. Information on socio-demographic data and health-related quality of life (HRQOL) (HUI3, EQ-5D, WOMAC and VF-14) was obtained through a telephone interview with patients. The correlation coefficients between the prioritisation instrument score and VAS and HRQOL were calculated. For the reliability study a self-administered questionnaire, which included hypothetic patients' scenarios, was sent via postal mail to the doctors. The priority of these scenarios was assessed through the prioritisation instrument. The intraclass correlation coefficient (ICC) between doctors was calculated.<h4>Results</h4>Correlations with VAS were strong for the AI (0.64, CI95%: 0.59-0.68) and for the CI (0.65, CI95%: 0.62-0.69), and moderate between the WOMAC and the AI (0.39, CI95%: 0.33-0.45) and the VF-14 and the CI (0.38, IC95%: 0.33-0.43). The results of the discriminant analysis were in general as expected. Inter-observer reliability was 0.79 (CI95%: 0.64-0.94) for the AI, and 0.79 (CI95%: 0.63-0.95) for the CI.<h4>Conclusion</h4>The results show acceptable validity and reliability of the prioritisation instruments in establishing priority for surgery.
Project description:<h4>Importance</h4>With continuing improvements in medical devices and more than a decade since the 2006 United Network for Organ Sharing (UNOS) allocation policy, it is pertinent to assess survival among patients on the heart transplantation waiting list, especially given the recently approved 2018 UNOS allocation policy.<h4>Objectives</h4>To assess survival outcomes among patients on the heart transplant waiting list during the past 3 decades and to examine the association of ventricular assist devices (VADs) and the 2006 UNOS allocation policy with survival.<h4>Design, setting, and participants</h4>A retrospective cross-sectional used the UNOS database to perform an analysis of 95 323 candidates wait-listed for heart transplantation between January 1, 1987, and December 29, 2017. Candidates for all types of combined transplants were excluded (n = 2087). Patients were followed up from the time of listing to death, transplantation, or removal from the list due to clinical improvement. Competing-risk, Kaplan-Meier, and multivariable Cox proportional hazards regression analyses were used.<h4>Main outcomes and measures</h4>The analysis involved an unadjusted and adjusted survival analysis in which the primary outcome was death on the waiting list. Because of changing waiting list preferences and policies during the study period, the intrinsic risk of death for wait-listed candidates was assessed by individually analyzing, comparing, and adjusting for several candidate risk factors.<h4>Results</h4>In total, 95 323 candidates (72 915 men [76.5%]; mean [SD] age, 51.9 [12.0] years) were studied. In the setting of changes in listing preferences, 1-year survival on the waiting list increased from 34.1% in 1987-1990 to 67.8% in 2011-2017 (difference in proportions, 0.34%; 95% CI, 0.32%-0.36%; P < .001). The 1-year waiting list survival for candidates with VADs increased from 10.2% in 1996-2000 to 70.0% in 2011-2017 (difference in proportions, 0.60%; 95% CI, 0.58%-0.62%; P < .001). Similarly, in the setting of changing mechanical circulatory support indications, the 1-year waiting list survival for patients without VADs increased from 53.9% in 1996-2000 to 66.5% in 2011-2017 (difference in proportions, 0.13%; 95% CI, 0.12%-0.14%; P < .001). In the decade prior to the 2006 UNOS allocation policy, the 1-year waiting list survival was 51.1%, while in the decade after it was 63.9% (difference in proportions, 0.13%; 95% CI, 0.12%-0.14%; P < .001). In adjusted analysis, each time period after 1987-1990 had a marked decrease in waiting list mortality.<h4>Conclusions and relevance</h4>This study found temporally associated increases in heart transplant waiting list survival for all patient groups (with or without VADs, UNOS status 1 and status 2 candidates, and candidates with poor functional status).