OBJECTIVES: Despite significant awareness on the value of leveraging patient relationships across the healthcare continuum, there is no research on the potential of using Electronic Health Record (EHR) systems to store structured patient relationship data, or its impact on enabling better healthcare. We sought to identify which EHR systems supported effective patient relationship data collection, and for systems that do, what types of relationship data is collected, how this data is used, and the perceived value of doing so. MATERIALS AND METHODS: We performed a literature search to identify EHR systems that supported patient relationship data collection. Based on our results, we defined attributes of an effective patient relationship model. The Open Medical Record System (OpenMRS), an open source medical record platform for underserved settings met our eligibility criteria for effective patient relationship collection. We performed a survey to understand how the OpenMRS patient relationship model was used, and how it brought value to implementers. RESULTS: The OpenMRS patient relationship model has won widespread adoption across many implementations and is perceived to be valuable in enabling better health care delivery. Patient relationship information is widely used for community health programs and enabling chronic care. Additionally, many OpenMRS implementers were using this feature to collect custom relationship types for implementation specific needs. CONCLUSIONS: We believe that flexible patient relationship data collection is critical for better healthcare, and can inform community care and chronic care initiatives across the world. Additionally, patient relationship data could also be leveraged for many other initiatives such as patient centric care and in the field of precision medicine.
OBJECTIVES: Despite significant awareness on the value of leveraging patient relationships across the healthcare continuum, there is no research on the potential of using Electronic Health Record (EHR) systems to store structured patient relationship data, or its impact on enabling better healthcare. We sought to identify which EHR systems supported effective patient relationship data collection, and for systems that do, what types of relationship data is collected, how this data is used, and the perceived value of doing so. MATERIALS AND METHODS: We performed a literature search to identify EHR systems that supported patient relationship data collection. Based on our results, we defined attributes of an effective patient relationship model. The Open Medical Record System (OpenMRS), an open source medical record platform for underserved settings met our eligibility criteria for effective patient relationship collection. We performed a survey to understand how the OpenMRS patient relationship model was used, and how it brought value to implementers. RESULTS: The OpenMRS patient relationship model has won widespread adoption across many implementations and is perceived to be valuable in enabling better health care delivery. Patient relationship information is widely used for community health programs and enabling chronic care. Additionally, many OpenMRS implementers were using this feature to collect custom relationship types for implementation specific needs. CONCLUSIONS: We believe that flexible patient relationship data collection is critical for better healthcare, and can inform community care and chronic care initiatives across the world. Additionally, patient relationship data could also be leveraged for many other initiatives such as patient centric care and in the field of precision medicine.
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Keywords:
Electronic health records and systems; care coordination; chronic disease; family relations; parent-child relations; patient-centered care; professional-family relations
Authors: William M Tierney; Marion Achieng; Elaine Baker; April Bell; Paul Biondich; Paula Braitstein; Daniel Kayiwa; Sylvester Kimaiyo; Burke Mamlin; Brian McKown; Nicholas Musinguzi; Winstone Nyandiko; Joseph Rotich; John Sidle; Abraham Siika; Martin Were; Ben Wolfe; Kara Wools-Kaloustian; Ada Yeung; Constantin Yiannoutsos Journal: Stud Health Technol Inform Date: 2010
Authors: Stephen M Petterson; Winston R Liaw; Robert L Phillips; David L Rabin; David S Meyers; Andrew W Bazemore Journal: Ann Fam Med Date: 2012 Nov-Dec Impact factor: 5.166