Gregory L Alexander1, Kimberly Powell1,2, Chelsea B Deroche3, Lori Popejoy1, Abu Saleh Mohammad Mosa4, Richelle Koopman5, Lorren Pettit6, Michelle Dougherty7. 1. Sinclair School of Nursing, University of Missouri, Columbia, Missouri, USA. 2. College of Nursing, University of Tennessee, Knoxville, Tennessee, USA. 3. Biostatistics & Research Design Unit, School of Medicine, University of Missouri, Columbia, Missouri, USA. 4. School of Medicine, University of Missouri, Columbia, Missouri, USA. 5. Family and Community Medicine, University of Missouri, Columbia, Missouri, USA. 6. Healthcare Information and Management Systems Society, Chicago, Illinois, USA. 7. Research Triangle Institute, Research Triangle Park, North Carolina, USA.
Abstract
OBJECTIVES: We describe the development of a nursing home information technology (IT) maturity model designed to capture stages of IT maturity. MATERIALS AND METHODS: This study had 2 phases. The purpose of phase I was to develop a preliminary nursing home IT maturity model. Phase II involved 3 rounds of questionnaires administered to a Delphi panel of expert nursing home administrators to evaluate the validity of the nursing home IT maturity model proposed in phase I. RESULTS: All participants (n = 31) completed Delphi rounds 1-3. Over the 3 Delphi rounds, the nursing home IT maturity staging model evolved from a preliminary, 5-stage model (stages 1-5) to a 7-stage model (stages 0-6). DISCUSSION: Using innovative IT to improve patient outcomes has become a broad goal across healthcare settings, including nursing homes. Understanding the relationship between IT sophistication and quality performance in nursing homes relies on recognizing the spectrum of nursing home IT maturity that exists and how IT matures over time. Currently, no universally accepted nursing home IT maturity model exists to trend IT adoption and determine the impact of increasing IT maturity on quality. CONCLUSIONS: A 7-stage nursing home IT maturity staging model was successfully developed with input from a nationally representative sample of U.S. based nursing home experts. The model incorporates 7-stages of IT maturity ranging from stage 0 (nonexistent IT solutions or electronic medical record) to stage 6 (use of data by resident or resident representative to generate clinical data and drive self-management).
OBJECTIVES: We describe the development of a nursing home information technology (IT) maturity model designed to capture stages of IT maturity. MATERIALS AND METHODS: This study had 2 phases. The purpose of phase I was to develop a preliminary nursing home IT maturity model. Phase II involved 3 rounds of questionnaires administered to a Delphi panel of expert nursing home administrators to evaluate the validity of the nursing home IT maturity model proposed in phase I. RESULTS: All participants (n = 31) completed Delphi rounds 1-3. Over the 3 Delphi rounds, the nursing home IT maturity staging model evolved from a preliminary, 5-stage model (stages 1-5) to a 7-stage model (stages 0-6). DISCUSSION: Using innovative IT to improve patient outcomes has become a broad goal across healthcare settings, including nursing homes. Understanding the relationship between IT sophistication and quality performance in nursing homes relies on recognizing the spectrum of nursing home IT maturity that exists and how IT matures over time. Currently, no universally accepted nursing home IT maturity model exists to trend IT adoption and determine the impact of increasing IT maturity on quality. CONCLUSIONS: A 7-stage nursing home IT maturity staging model was successfully developed with input from a nationally representative sample of U.S. based nursing home experts. The model incorporates 7-stages of IT maturity ranging from stage 0 (nonexistent IT solutions or electronic medical record) to stage 6 (use of data by resident or resident representative to generate clinical data and drive self-management).
Authors: Gregory L Alexander; Richard W Madsen; Erin L Miller; Melissa K Schaumberg; Allison E Holm; Rachel L Alexander; Keely K Wise; Michelle L Dougherty; Brian Gugerty Journal: J Am Med Inform Assoc Date: 2016-04-23 Impact factor: 4.497
Authors: Daniel Walker; Arthur Mora; Mollye M Demosthenidy; Nir Menachemi; Mark L Diana Journal: Health Aff (Millwood) Date: 2016-03 Impact factor: 6.301
Authors: Gregory L Alexander; Chelsea Deroche; Kimberly Powell; Abu Saleh Mohammad Mosa; Lori Popejoy; Richelle Koopman Journal: J Med Syst Date: 2020-02-05 Impact factor: 4.460
Authors: Gregory L Alexander; Chelsea Deroche; Kimberly Powell; Abu Saleh Mohammad Mosa; Lori Popejoy; Richelle Koopman Journal: J Med Syst Date: 2020-02-26 Impact factor: 4.460