How language barriers influence provider workload for home health care professionals: A secondary analysis of interview data.

BACKGROUND

Increasingly, patients with limited English proficiency are accessing home health care services in the United States. Few studies have examined how language barriers influence provider role implementation or workload in the home health care setting.

OBJECTIVES

To explore home health care professionals' perspectives about how workload changes from managing language barriers influence quality and safety in home health care.

DESIGN

A qualitative secondary data analysis using a summative content analysis approach was used to analyze existing semi-structured interview data.

SETTING

A large urban home health care agency located on the East Coast of the United States.

PARTICIPANTS

Thirty five home health care providers [31 registered nurses, 3 physical therapists, 1 occupational therapist].

RESULTS

A total of 142 discrete incidents emerged from the analysis. Overall, home health care providers experienced distinct shifts in how they implemented their roles that added to their workload and time spent with Limited English Proficiency patients and family members. Providers were concerned about interpretation accuracy and perceived it as potentially posing risks to patient safety. Changes in work patterns, therefore, sought to maximize patient safety.

CONCLUSIONS

Home health care providers decision-making about how they adapt practice when faced with a language barrier is a sequence of actions based on awareness of the patient's language preference and if they spoke another language. Subsequent choices showed proactive behaviors to manage increased workload shaped by their perceived risk of the threats posed by the quality of interpreter services. Future research should develop quantitative models examining differences in workload when caring for limited English proficiency versus English speaking patients as well as the relationship between visit length and patient outcomes to determine optimal quality models.