Primary Care in the Spinal Cord Injury Population: Things to Consider in the Ongoing Discussion

Unmet healthcare needs, access to services and experiences with health providers among persons with spinal cord injury in Australia

STUDY DESIGN

Cross-sectional survey.

OBJECTIVES

Appropriate and timely lifelong access to healthcare following a spinal cord injury (SCI) is critical, yet unmet healthcare needs in this population are common. Poor experiences with healthcare providers can be a barrier to health-seeking behaviour, and we hypothesised that there would be an association between unmet healthcare needs and care experiences. This study aimed to: (1) describe healthcare provider utilisation in the past year, unmet care needs and satisfaction with healthcare services; (2) explore the association between experiences with healthcare providers and unmet healthcare needs; and (3) explore the association between healthcare provider utilisation and participant characteristics, including unmet healthcare needs.

SETTING

Community.

METHODS

Analysis of data for 1579 Australians aged ≥ 18, who were ≥ 1-year post-SCI and living in the community. Bayesian penalised regression was used to model six binary outcomes: unmet healthcare needs; the use of general practitioners (GPs), allied health practitioners, rehabilitation specialists; medical specialists; and hospitalisations in the past 12-months.

RESULTS

Unmet needs were reported by 17% of participants, with service cost the common deterrent. There was evidence of an effect for provider experiences on unmet healthcare needs, but no evidence that unmet healthcare needs was associated with the use of GPs, allied health practitioners, and rehabilitation or medical specialists.

CONCLUSIONS

Unmet healthcare needs were reported in the context of high healthcare use and large proportions of secondary conditions in a cohort with long-term SCI. Improved health access for people with SCI include better primary-secondary care collaboration is needed.

Developing and Evaluating Data Infrastructure and Implementation Tools to Support Cardiometabolic Disease Indicator Data Collection

Background

Assessment of aerobic exercise (AE) and lipid profiles among individuals with spinal cord injury or disease (SCI/D) is critical for cardiometabolic disease (CMD) risk estimation.

Objectives

To utilize an artificial intelligence (AI) tool for extracting indicator data and education tools to enable routine CMD indicator data collection in inpatient/outpatient settings, and to describe and evaluate the recall of AE levels and lipid profile assessment completion rates across care settings among adults with subacute and chronic SCI/D.

Methods

A cross-sectional convenience sample of patients affiliated with University Health Network's SCI/D rehabilitation program and outpatients affiliated with SCI Ontario participated. The SCI-HIGH CMD intermediary outcome (IO) and final outcome (FO) indicator surveys were administered, using an AI tool to extract responses. Practice gaps were prospectively identified, and implementation tools were created to address gaps. Univariate and bivariate descriptive analyses were used.

Results

The AI tool had <2% error rate for data extraction. Adults with SCI/D (n = 251; 124 IO, mean age 61; 127 FO, mean age 55; p = .004) completed the surveys. Fourteen percent of inpatients versus 48% of outpatients reported being taught AE. Fifteen percent of inpatients and 51% of outpatients recalled a lipid assessment (p < .01). Algorithms and education tools were developed to address identified knowledge gaps in patient AE and lipid assessments.

Conclusion

Compelling CMD health service gaps warrant immediate attention to achieve AE and lipid assessment guideline adherence. AI indicator extraction paired with implementation tools may facilitate indicator deployment and modify CMD risk.