Evaluation of Complexity Measurement Tools for Correlations with Health-Related Outcomes, Health Care Costs and Impacts on Healthcare Providers: A Scoping Review

Abstract

Various tools to measure patient complexity have been developed. Primary care physicians often deal with patient complexity. However, their usefulness in primary care settings is unclear. This study explored complexity measurement tools in general adult and patient populations to investigate the correlations between patient complexity and outcomes, including health-related patient outcomes, healthcare costs, and impacts on healthcare providers. We used a five-stage scoping review framework, searching MEDLINE and CINAHL, including reference lists of identified studies. A total of 21 patient complexity management tools were found. Twenty-five studies examined the correlation between patient complexity and health-related patient outcomes, two examined healthcare costs, and one assessed impacts on healthcare providers. No studies have considered sharing information or action plans with multidisciplinary teams while measuring outcomes for complex patients. Of the tools, eleven used face-to-face interviews, seven extracted data from medical records, and three used self-assessments. The evidence of correlations between patient complexity and outcomes was insufficient for clinical implementation. Self-assessment tools might be convenient for conducting further studies. A multidisciplinary approach is essential to develop effective intervention protocols. Further research is required to determine these correlations in primary care settings.

Gender‐specific factors associated with case complexity in middle‐aged and older adults—Evidence from a large population‐based study

“…Trained healthcare professionals can conduct and rate the INTERMED within 15–20 min. According to recent reviews, 11,12 the INTERMED is one of the best studied tools for observer‐rated assessment of complexity. It has proven to be a powerful diagnostic tool and has successfully been applied to identify complex individuals in clinical 19 and epidemiological settings 4,13 .…”

“…8,9 Among screening tools for patient complexity, the INTERMED interview 10 is one of the best studied, valid, and reliable instruments for this purpose. 11,12 It applies a holistic, bio-psycho-social approach to identify complex patients. The INTERMED for the elderly (IM-E) is an adapted version specifically for use in elderly populations 5 and has proven to be well suited in an epidemiological context.…”

“…Among screening tools for patient complexity, the INTERMED interview 10 is one of the best studied, valid, and reliable instruments for this purpose 11,12 . It applies a holistic, bio‐psycho‐social approach to identify complex patients.…”

Advancing health equity for Indigenous peoples in Canada: development of a patient complexity assessment framework

“…Given that health inequities across large populations, such as Indigenous peoples, can translate into a higher burden on healthcare systems by way of taxing limited available resources [ 65 ], identifying and acting on patient complexity to improve care may feasibly promote better resource allocation to meet patient needs. Patients with complexity often require interventions that are beyond the scope of typical biomedical care and the training of most HCPs [ 63 ], which is compounded by healthcare systems being ill-equipped to provide HCPs the appropriate resources necessary to care for patients [ 64 ].…”

“…PCATs follow two common formats, these being: i) a face-to-face interview between a patient and provider where the provider determines the responses within the PCAT, or ii) for patients to complete in written or digital form on their own in a self-assessment method [ 65 ]. PCATs may be specific to in-patient or outpatient populations, and in the case of some populations such as the elderly, PCATs may be applicable to those outside of healthcare settings too [ 65 , 71 , 72 ]. Though PCATs present great utility in identifying patient needs, sources of their complexity, and their resources, limited longitudinal data supports their utility beyond these.…”