Waiting time prioritisation for specialist services in Italy: The homogeneous waiting time groups approach

Urgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With Echocardiograms

BACKGROUND

In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care. For example, an echocardiogram is a type of laboratory test that is extremely important and not easily accessible. The increasing demand for echocardiograms underscores the imperative for more efficient scheduling protocols. Despite this pressing need, limited research has been conducted in this area.

OBJECTIVE

The study aims to develop an interpretable machine learning model for determining the urgency of patients requiring echocardiograms, thereby aiding in the prioritization of scheduling procedures. Furthermore, this study aims to glean insights into the pivotal attributes influencing the prioritization of echocardiogram appointments, leveraging the high interpretability of the machine learning model.

METHODS

Empirical and predictive analyses have been conducted to assess the urgency of patients based on a large real-world echocardiogram appointment dataset (ie, 34,293 appointments) sourced from electronic health records encompassing administrative information, referral diagnosis, and underlying patient conditions. We used a state-of-the-art interpretable machine learning algorithm, the optimal sparse decision tree (OSDT), renowned for its high accuracy and interpretability, to investigate the attributes pertinent to echocardiogram appointments.

RESULTS

The method demonstrated satisfactory performance (F1-score=36.18% with an improvement of 1.7% and F2-score=28.18% with an improvement of 0.79% by the best-performing baseline model) in comparison to the best-performing baseline model. Moreover, due to its high interpretability, the results provide valuable medical insights regarding the identification of urgent patients for tests through the extraction of decision rules from the OSDT model.

CONCLUSIONS

The method demonstrated state-of-the-art predictive performance, affirming its effectiveness. Furthermore, we validate the decision rules derived from the OSDT model by comparing them with established medical knowledge. These interpretable results (eg, attribute importance and decision rules from the OSDT model) underscore the potential of our approach in prioritizing patient urgency for echocardiogram appointments and can be extended to prioritize other laboratory test appointments using electronic health record data.

The Role of Homogeneous Waiting Group Criteria in Patient Referrals: Views of General Practitioners and Specialists in South Tyrol, Italy

Homogeneous waiting group (HWG) criteria are central to the patient referral process, guiding primary care physicians and hospitalists in directing patient care to specialists. This cross-sectional observational study, conducted in South Tyrol, Italy, in 2023, aimed to assess the implementation and impact of HWG criteria on healthcare from the perspective of general practitioners and hospital physicians. A questionnaire was developed to gain knowledge about referral practices as perceived by general practitioners and specialists. The survey included 313 participants (82 general practitioners and 231 hospital physicians) and was designed to capture a range of factors influencing the application of HWG criteria, including communication and collaboration practices. The results showed moderate levels of familiarity with HWG criteria and opinions about the need for criteria refinement among hospitalists, indicating that further education and refinement of these criteria are warranted. Both general practitioners and hospital physicians expressed dissatisfaction with the current specialist referral system, highlighting the significant gaps in effective communication and collaboration. The survey also demonstrated the influence of patient demands and waiting times on referral practices, and the need for streamlined and accessible specialist care. This study highlights the need for improvement and adaptation of HWG criteria to better meet the needs of healthcare providers and patients in South Tyrol. By addressing the identified gaps in communication, collaboration, and education related to the HWG system, the efficiency, effectiveness, and patient-centeredness of the referral process can be improved, ultimately leading to better health outcomes.

A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals

BACKGROUND

This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases.

METHODS

A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region.

RESULTS

The NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals' texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals' texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas.

CONCLUSIONS

The use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation.

Consensus among clinicians on referrals' priority and use of digital decision-making support systems

The growing demand for referrals is a main policy concern in health systems. One approach involves the development of demand management tools in the form of clinical prioritization to regulate patient referrals from primary care to specialist care. For clinical prioritization to be effective, it is critical that general practitioners (GPs) assess patient priority in the same way as specialists. The progressive development of IT tools in clinical practice, in the form of electronic referrals support systems (e-RSS), can facilitate clinical prioritization. In this study, we tested if higher use of e-RSS or higher use of high-priority categories was associated with the degree of agreement and therefore consensus on clinical priority between GPs and specialists. We found that higher use by GPs of the e-RSS tool was positively associated with greater degree of priority agreement with specialists, while higher use of the high-priority categories was associated with lower degree of priority agreement with specialists. Furthermore, female GPs, GPs in association with others, and GPs using a specific electronic medical record showed higher agreement with specialists. Our study therefore supports the use of electronic referrals systems to improve clinical prioritization and manage the demand of specialist visits and diagnostic tests. It also shows that there is scope for reducing excessive use by GPs of high-priority categories.

Inequality in Healthcare Utilization in Italy: How Important Are Barriers to Access?

With the ageing population, equitable access to medical care has proven to be paramount for the effective and efficient management of all diseases. Healthcare access can be hindered by cost barriers for drugs or exams, long waiting lists or difficult access to the place where the needed healthcare service is provided. The aim of this paper is to investigate whether the probability of facing one of these barriers varies among individuals with different socio-economic status and care needs, controlling for geographical variability.

METHODS

The sample for this study included 9629 interviews with Italian individuals, aged 15 and over, from the second wave (2015) of the European Health Interview Survey, which was conducted in all EU Member States. To model barriers to healthcare, two-level variance components of logistic regression models with a nested structure given by the four Italian macro-areas were considered.

RESULTS

Of the barriers considered in this study, only two were found to be significantly associated with healthcare utilization. Specifically, they are long waiting lists for specialist service accessibility (adjOR = 1.20, 95% CI (1.07; 1.35)) and very expensive exams for dental visit accessibility (adjOR = 0.84, 95% CI (0.73; 0.96)). Another important result was the evidence of an increasing north-south gradient for all of the considered barriers.

CONCLUSION

In Italy, healthcare access is generally guaranteed for all of the services, except for specialist and dental visits that face a waiting time and financial barriers. However, barriers to healthcare were differentiated by income and sex. The north-south gradient for healthcare utilization could be explained through the existing differences in organizational characteristics of the several regional healthcare services throughout Italy.

Exploring the implementation and underlying mechanisms of centralized referral systems to access specialized health services in Quebec

BACKGROUND

In 2016, Quebec, a Canadian province, implemented a program to improve access to specialized health services (Accès priorisé aux services spécialisés (APSS)), which includes single regional access points for processing requests to such services via primary care (Centre de répartition des demandes de services (CRDS)). Family physicians fill out and submit requests for initial consultations with specialists using a standardized form with predefined prioritization levels according to listed reasons for consultations, which is then sent to the centralized referral system (the CRDS) where consultations with specialists are assigned. We 1) described the APSS-CRDS program in three Quebec regions using logic models; 2) compared similarities and differences in the components and processes of the APSS-CRDS models; and 3) explored contextual factors influencing the models' similarities and differences.

METHODS

We relied on a qualitative study to develop logic models of the implemented APSS-CRDS program in three regions. Semi-structured interviews with health administrators (n = 9) were conducted. The interviews were analysed using a framework analysis approach according to the APSS-CRDS's components included in the initially designed program, Mitchell and Lewis (2003)'s logic model framework, and Chaudoir and colleagues (2013)'s framework on contextual factors' influence on an innovation's implementation.

RESULTS

Findings show the APSS-CRDS program's regional variability in the implementation of its components, including its structure (centralized/decentralized), human resources involved in implementation and operation, processes to obtain specialists' availability and assess/relay requests, as well as monitoring methods. Variability may be explained by contextual factors' influence, like ministerial and medical associations' involvement, collaborations, the context's implementation readiness, physician practice characteristics, and the program's adaptability.

INTERPRETATION

Findings are useful to inform decision-makers on the design of programs like the APSS-CRDS, which aim to improve access to specialists, the essential components for the design of these types of interventions, and how contextual factors may influence program implementation. Variability in program design is important to consider as it may influence anticipated effects, a next step for the research team. Results may also inform stakeholders should they wish to implement similar programs to increase access to specialized health services via primary care.

Designing centralized waiting lists for attachment to a primary care provider: Considerations from a logic analysis

Access to a regular primary care provider is essential to quality care. In Canada, where 15 % of patients are unattached (i.e., without a regular provider), centralized waiting lists (CWLs) help attach patients to a primary care provider (family physician or nurse practitioner). Previous studies reveal mechanisms needed for CWLs to work, but focus mostly on CWLs for specialized health care. We aim to better understand how to design CWLs for unattached patients in primary care. In this study, a logic analysis compares empirical evidence from a qualitative case study of CWLs for unattached patients in seven Canadian provinces to programme theory derived from a realist review on CWLs. Data is analyzed using context-intervention-mechanism-outcome configurations. Results identify mechanisms involved in three components of CWL design: patient registration, patient prioritization, and patient assignment to a provider for attachment. CWL programme theory is revised to integrate mechanisms specific to primary care, where patients, rather than referring providers, are responsible for registering on the CWL, where prioritization must consider a broad range of conditions and characteristics, and where long-term acceptability of attachment is important. The study provides new insight into mechanisms that enable CWLs for unattached patients to work.

Factors affecting the choices of adoption/non-adoption of future technologies during coronavirus pandemic

The literature describes the potential for using future services technologies in public health emergencies. The ongoing coronavirus pandemic is resulting in unparalleled challenges to healthcare services in almost all countries, requiring innovative methods of practicing across health professions. Factors affecting pharmacists' choice of telemedicine adoption/non-adoption are yet to be examined, especially in Italy. Thus, we investigate the behavioral intentions of pharmacists related to telemedicine, as a future services technology, in the current pandemic context. Our model draws on the theory of planned behavior and extends it to investigate the mechanisms underlying attitude formation to telemedicine adoption through a cross-sectional approach, using a questionnaire-based survey. The model has medium-to-high power in predicting telemedicine adoption intention, and the two significant direct antecedents of the target construct (attitude to telemedicine, and perceived behavioral control) are almost equally important. The psychological mechanisms linked to the tendency to implement emerging technology are complex and have major management effects. Studies in this field are yet to focus on the issues that affect the pharmacists' decision regarding adopting or not adopting telemedicine, as a future services technology.

How the design and implementation of centralized waiting lists influence their use and effect on access to healthcare - A realist review

CONTEXT

Many health systems have centralized waiting lists (CWLs), but there is limited evidence on CWL effectiveness and how to design and implement them.

AIM

To understand how CWLs' design and implementation influence their use and effect on access to healthcare.

METHODS

We conducted a realist review (n = 21 articles), extracting context-intervention-mechanism-outcome configurations to identify demi-regularities (i.e., recurring patterns of how CWLs work).

RESULTS

In implementing non-mandatory CWLs, acceptability to providers influences their uptake of the CWL. CWL eligibility criteria that are unclear or conflict with providers' role or judgement may result in inequities in patient registration. In CWLs that prioritize patients, providers must perceive the criteria as clear and appropriate to assess patients' level of need; otherwise, prioritization may be inconsistent. During patients' assignment to service providers, providers may select less-complex patients to obtain CWLs rewards or avoid penalties; or may select patients for other policies with stronger incentives, disregarding the established patient order and leading to inequities and limited effectiveness.

CONCLUSION

These findings highlight the need to consider provider behaviours in the four sequential CWL design components: CWL implementation, patient registration, patient prioritization and patient assignment to providers. Otherwise, CWLs may result in limited effects on access or lead to inequities in access to services.

Priority and appropriateness of upper endoscopy out-patient referrals: Two-period comparison in an open-access unit

BACKGROUND

In the early 2000s we introduced a prioritization model for referrals based on involvement of primary care physicians (PCPs) and specialists.

AIMS

Assess the application of that model of prioritisation, comparing gastroscopies performed 8 years apart, with respect to priority level, appropriateness and relevant endoscopic findings (REFs).

METHODS

The studies included 247 and 354 out-patients, who had undergone gastroscopy in 2006 and in 2014, respectively. To reduce interspecialists variability, both studies were performed by the same specialist as investigator.

RESULTS

In both years, most patients were assigned low-priority referral by PCPs (78.6% and 75.1% respectively). The agreement PCPs versus specialist on referral priority was moderate in 2006 (0.60, Landis-Koch scale 0.41-0.60) and high in 2014 (0.81, Landis-Koch scale 0.81-1.00). In both years we observed a similar rate of inappropriateness: 27.5% and 27.1%, respectively. Due to multiple logistic regression, the odds ratio (OR) for REF increased when: (i) very high-priority referral versus nopriority referral was indicated (8.813 OR, p = 0.0012), (ii) referral followed the guidelines (9.29 OR, p<0.0001), and (iii) agreement of priority occurred (1.911 OR, p = 0.0308).

CONCLUSIONS

Our findings highlighted that the issues of low-priority referrals should be addressed in order to discontinue gastroscopy overusing and reduce related operational costs.

Time to wait: a systematic review of strategies that affect out-patient waiting times

Objective Out-patient waiting times pose a significant challenge for public patients in need of specialist evaluation and intervention. The aim of the present study was to identify and categorise effective strategies to reduce waiting times for specialist out-patient services with a focus on the Australian healthcare system. Methods A systematic review of major health databases was conducted using the key terms 'outpatient*' AND 'waiting time', 'process*' AND 'improvement in outpatient clinics'. Identified articles were assessed for their relevance by sequential review of the title, abstract and full text. References of the selected manuscripts were scanned for additional relevant articles. Selected articles were evaluated for consistent and emerging themes. Results In all, 152 articles were screened, of which 38 were included in the present review. Numerous strategies identified in the articles were consolidated into 26 consistent approaches. Three overarching themes were identified as significantly affecting waiting times: resource realignment, operational efficiency and process improvement. Conclusions Strategies to align resources, increase operational efficiency and improve processes provide a comprehensive approach that may reduce out-patient waiting times. What is known about the topic? Out-patient waiting times are a challenge in most countries that seek to provide universal access to health care for all citizens. Although there has been extensive research in this area, many patients still experience extensive delays accessing specialist care, particularly in the public health sector. The multiple factors that contribute to bottlenecks and inefficiencies in the referral process and affect patient waiting times are often poorly understood. What does this paper add? This paper reviews the published healthcare literature to identify strategies that affect specialist out-patient waiting times for patients. The findings suggest that there are numerous operational strategies that affect waiting times. These strategies may be categorised into three overarching themes (resource alignment, operational efficiencies and out-patient processes) that, when actioned in a coordinated approach, have the potential to significantly reduce out-patient waiting times. What are the implications for practitioners? This paper identifies evidence-based strategies for aligning resources, improving operational efficiency and streamlining processes, which may provide improvements to specialist out-patient waiting times for patients. Addressing the identified organisational, person-related, cultural and attitudinal factors will assist health system managers and health practitioners target the most appropriate improvement activities to reduce waiting times.

The effect of waiting times on demand and supply for elective surgery: Evidence from Italy

Waiting times are a major policy concern in publicly funded health systems across OECD countries. Economists have argued that, in the presence of excess demand, waiting times act as nonmonetary prices to bring demand for and supply of health care in equilibrium. Using administrative data disaggregated by region and surgical procedure over 2010-2014 in Italy, we estimate demand and supply elasticities with respect to waiting times. We employ linear regression models with first differences and instrumental variables to deal with endogeneity of waiting times. We find that demand is inelastic to waiting times while supply is more elastic. Estimates of demand elasticity are between -0.15 to -0.24. Our results have implications on the effectiveness of policies aimed at increasing supply and their ability to reduce waiting times.