Table 1.

Summary of the included articles.

Reference	Tool	Country	Study design	Objectives	Aim of the classification system	Comparator/proxy measure of complexity	Population and size	Percentage of patients identified according to complexity rating
Busquet-Duran et al.17	HexCom	Spain	Cross sectional	To describe differences in complexity across disease groups in specific home care for advanced disease and/or at the end-of-life patients, both in general and as relates to each domain and subdomain.	The instrument defines situations that are refractory to treatment options as ‘high complexity’, and situations that are difficult to resolve as ‘moderate complexity’. In this sense, classifying patients according to the level of complexity they present helps distinguish between those who need specialised palliative care and those who do not.	–	Community patients with advanced disease or at the end of life (N = 832)	High complexity 42.4%. Medium complexity 47%. Low complexity 10.2%
Dos Santos et al.18	Perroca scale	Brazil	Retrospective cohort	To evaluate the complexity of care, using the Perroca scale, as well as discharges and deaths that occurred in the period.	Is a system for evaluating complexity of care, and a management tool for planning nursing care	–	Inpatient palliative care unit (N = 1568)	Four levels of complexity. – Minimal complexity 22% – Intermediate care 38% – Semi-intensive care 36% – Intensive care 4%
Eagar et al.20	AN-SNAP	Australia	Cross sectional analysis	To assess the ability of commonly used patient measures to predict the resource consumption of sub-acute and non-acute patients	To classify patient casemix according to predictors of use of resources.	Health service usage – staff time, recourse consumption, services.	Palliative care N = 3104 episodes.	–
Eagar et al.8	AN-SNAP	Australia, New Zealand	Cross sectional analysis	To provide a nontechnical discussion of the development of a palliative care casemix classification and some policy implications of its implementation.	To classify patient casemix according to resource usage.	Health service usage – staff time, recourse consumption, services.	Inpatient and community palliative care in Australia and New Zealand. 3866 patients, 4596 episodes of care	–
Eagar et al.21	AN-SNAP	Australia, New Zealand	Cross sectional analysis	To develop a palliative care casemix classification for use in all settings including hospital, hospice and home-based care.	To classify patient casemix according to resource usage.	Health service usage – staff time, recourse consumption, services.	Inpatient and community palliative care in Australia and New Zealand. 3866 patients, 4596 episodes of care	–
Green and Gordon22	AN-SNAP	Australia, New Zealand	Cross sectional analysis	To describe the first review of the AN-SNAP casemix classification.	To classify patient casemix for sub- and non-acute care that reflected costs and resource usage.	Classification system, health resource usage	2 cohorts of inpatient and community palliative care N = 1868 episodes and N = 10,505 episodes	–
Hui et al.23		USA	Delphi	To develop consensus on a list of criteria for referral of patients with advanced cancer at secondary or tertiary care hospitals to outpatient palliative care.	To optimise the use of scarce palliative-care resources, patients need to be referred at the right time for the right reasons. Therefore, consensus on simple, robust, and valid referral criteria is urgently needed.	–	–	–
Martin-Rosello et al.19	IDC-Pal	Spain	Review article	To presents the state of art of the role of complexity in specialist palliative care provision.	To diagnose and stratify complexity. It was designed to maximise consensus among professionals of the different level of care provision, facilitate effective communication between resources, and enhance a shared care model for palliative care.	–	–	–
Tuca et al.7	PALCOM	Spain	Prospective cohort	To identify the factors influencing level determination of complexity, propose predictive models, and build a complexity scale of PC.	To create a predictive model of PC complexity aimed at differentiating patients with low palliative complexity, in whom basic PC would be indicated, from patients with medium-high complexity who would require shared care with specialised PC teams.	Clinician grading of complexity	Advanced cancer patients (N = 324)	High complexity 41% Medium complexity 42.9% Low complexity 16.1%