Improving Language Services



Communication in a language patients can understand is fundamental for receiving and providing safe, high-quality health care. Research shows that the likely result of using untrained interpreters is an increase in medical errors, poorer patient-provider communication, and poorer follow-up and adherence to discharge instructions. Although hospitals are required to provide interpreter services to patients who speak limited English, there is little guidance on the best ways to implement these requirements. 

  • In this initiative, 1.5 million patients were screened for preferred spoken language, more than half a million screened for preferred written language, and more than 4,500 patients had qualified interpreters at both initial assessment and discharge.
  • Thirty-two teams representing 12 AF4Q Alliances participated in the Improving Language Services portion of this initiative.
    • Thirty hospitals improved their screening rates to determine a patient’s preferred spoken language for health care. St. Vincent Charity Hospital in Cleveland improved by 99 percent and Mid Coast Hospital in Maine improved by 98 percent.
    • Twenty five hospitals improved their rates of collecting and reporting a patient’s preferred written language for health care. Allegan Hospital, West Michigan; Monroe Clinic, Wisconsin; St. Elizabeth’s Medical Center, Boston; St. Vincent Charity Medical Center, Cleveland; Tacoma General Hospital, Puget Sound; Tufts Medical Center, Boston; and St. Mary’s Health Care in West Michigan demonstrated impressive improvement in this measure and achieved screening rates of over 95 percent.
    • Twenty hospitals improved in providing qualified language services at initial assessment and discharge. St. Luke’s Hospita in Kansas City and Providence St. Peter in Puget Sound both improved 58 percent.
  • By the end of the collaborative, 100 percent of the hospital teams in Improving Language Services had successfully standardized their registration systems to collect self-reported race, ethnicity, and language data.