Towards an implementation framework for business intelligence in healthcare

As healthcare organizations continue to be asked to do more with less, access to information is essential for sound evidence-based decision making. Business intelligence (BI) systems are designed to deliver decision-support information and have been repeatedly shown to provide value to organizations. Many healthcare organizations have yet to implement BI systems and no existing research provides a healthcare-specific framework to guide implementation. To address this research gap, we employ a case study in a Canadian Health Authority in order to address three questions: (1) what are the most significant adverse impacts to the organization's decision processes and outcomes attributable to a lack of decision-support capabilities? (2) what are the root causes of these impacts, and what workarounds do they necessitate? and (3) in light of the issues identified, what are the key considerations for healthcare organizations in the early stages of BI implementation? Using the concept of co-agency as a guide we identified significant decision-related adverse impacts and their root causes. We found strong management support, the right skill sets and an information-oriented culture to be key implementation considerations. Our major contribution is a framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes.

[1]  B MilesMatthew,et al.  Qualitative Data Analysis , 2009, Approaches and Processes of Social Science Research.

[2]  Tobias Bucher,et al.  Process-centric business intelligence , 2009, Bus. Process. Manag. J..

[3]  Daniel Amyot,et al.  Towards A Business Intelligence FrameworkFor Healthcare Safety , 2010 .

[4]  John Mylopoulos,et al.  Enterprise Modeling for Business Intelligence , 2010, PoEM.

[5]  Atish P. Sinha,et al.  An empirical investigation of the key determinants of data warehouse adoption , 2008, Decis. Support Syst..

[6]  Michael Stanek,et al.  Harnessing the Power of Enhanced Data for Healthcare Quality Improvement: Lessons from a Minnesota Hospital Association Pilot Project , 2012, Journal of healthcare management / American College of Healthcare Executives.

[7]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[8]  Anna Sidorova,et al.  Business Intelligence (Bi) Success and the Role of Bi Capabilities , 2011, Intell. Syst. Account. Finance Manag..

[9]  Charlene R. Weir,et al.  Characterizing "information transfer" by using a Joint Cognitive Systems model to improve continuity of care in the aged , 2012, Int. J. Medical Informatics.

[10]  G. Robert,et al.  Diffusion of innovations in service organizations: systematic review and recommendations. , 2004, The Milbank quarterly.

[11]  Efraim Turban,et al.  Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures , 2013 .

[12]  Ralph M Hanson Good health information--an asset not a burden! , 2011, Australian health review : a publication of the Australian Hospital Association.

[13]  Ming-Huei Hsieh,et al.  A case analysis of Savecom: The role of indigenous leadership in implementing a business intelligence system , 2010, Int. J. Inf. Manag..

[14]  E. Rogers Diffusion of Innovations , 1962 .

[15]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[16]  Amarnath Banerjee,et al.  Clinical decision support: Converging toward an integrated architecture , 2012, J. Biomed. Informatics.

[17]  Angela Mattia A Multi-Dimensional View Of Socio-Technical Information Systems Research And Technochange , 2011, BIS 2011.

[18]  C JonesMary,et al.  Business Intelligence (BI) Success And The Role Of BI Capabilities , 2011 .

[19]  Barbara Wixom,et al.  An Empirical Investigation of the Factors Affecting Data Warehousing Success , 2001, MIS Q..

[20]  J. Morse Qualitative data analysis (2nd ed): Mathew B. Miles and A. Michael Huberman. Thousand Oaks, CA: Sage Publications, 1994. Price: $65.00 hardback, $32.00 paperback. 238 pp , 1996 .

[21]  Marianne Bradford,et al.  Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems , 2003, Int. J. Account. Inf. Syst..

[22]  Hsiu-Fang Hsieh,et al.  Three Approaches to Qualitative Content Analysis , 2005, Qualitative health research.

[23]  Bonnie Kaplan,et al.  White Paper: Health IT Success and Failure: Recommendations from Literature and an AMIA Workshop , 2009, J. Am. Medical Informatics Assoc..

[24]  Aleš Popovič,et al.  Towards business intelligence systems success: Effects of maturity and culture on analytical decision making , 2012, Decis. Support Syst..

[25]  Marc Berg,et al.  Viewpoint Paper: Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors , 2003, J. Am. Medical Informatics Assoc..

[26]  Kathleen M. Carley,et al.  Using OrgAhead, a computational modeling program, to improve patient care unit safety and quality outcomes , 2005, Int. J. Medical Informatics.

[27]  Henri Barki,et al.  Explaining the Role of User Participation in Information System Use , 1994 .

[28]  David R. Firth,et al.  Communications of the Association for Information Systems , 2011 .

[29]  Robert P. Bostrom,et al.  MIS Problems and failures: a sociotechnical perspective part I: the cause , 1977 .

[30]  Xueyun Sharon Wang,et al.  Infrastructure for a clinical-decision-intelligence system , 2007, IBM Syst. J..

[31]  J. Flower,et al.  Who owns health care's most valuable information? , 2006, Physician executive.

[32]  Robyn Tamblyn,et al.  The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials , 2012, J. Am. Medical Informatics Assoc..

[33]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[34]  Alan R. Hevner,et al.  Design of an information volatility measure for health care decision making , 2012, Decis. Support Syst..

[35]  Andrew Georgiou,et al.  Understanding the information dynamics of medication administration in residential aged care facilities (RACFs): A prerequisite for design of effective ICT systems , 2013, CSHI.

[36]  Enrico W. Coiera,et al.  Supporting communication in health care , 2005, Int. J. Medical Informatics.

[37]  J. Braithwaite,et al.  Implementation of a patient safety incident management system as viewed by doctors, nurses and allied health professionals , 2009, Health.

[38]  Atish P. Sinha,et al.  A Model of Data Warehousing Process Maturity , 2012, IEEE Transactions on Software Engineering.

[39]  William Yeoh,et al.  Critical Success Factors for Business Intelligence Systems , 2010, J. Comput. Inf. Syst..

[40]  Tobias Mettler,et al.  Understanding business intelligence in the context of healthcare , 2009, Health Informatics J..

[41]  Michael I. Harrison,et al.  Viewpoint Paper: Unintended Consequences of Information Technologies in Health Care - An Interactive Sociotechnical Analysis , 2007, J. Am. Medical Informatics Assoc..

[42]  Deborah Crist-Grundman,et al.  Effective workforce management starts with leveraging technology, while staffing optimization requires true collaboration. , 2011, Nursing economic$.