Appl Clin Inform 2017; 08(02): 381-395
DOI: 10.4338/ACI-2016-11-RA-0191
Research Article
Schattauer GmbH

Using telephony data to facilitate discovery of clinical workflows

Donald W. Rucker
1   Departments of Biomedical Informatics and Emergency Medicine, Ohio State University, Columbus, OH, USA
› Author Affiliations
Further Information

Publication History

08 November 2016

13 February 2017

Publication Date:
21 December 2017 (online)

Summary

Background: Discovery of clinical workflows to target for redesign using methods such as Lean and Six Sigma is difficult. VoIP telephone call pattern analysis may complement direct observation and EMR-based tools in understanding clinical workflows at the enterprise level by allowing visualization of institutional telecommunications activity.

Objective: To build an analytic framework mapping repetitive and high-volume telephone call patterns in a large medical center to their associated clinical units using an enterprise unified communications server log file and to support visualization of specific call patterns using graphical networks.

Methods: Consecutive call detail records from the medical center’s unified communications server were parsed to cross-correlate telephone call patterns and map associated phone numbers to a cost center dictionary. Hashed data structures were built to allow construction of edge and node files representing high volume call patterns for display with an open source graph network tool.

Results: Summary statistics for an analysis of exactly one week’s call detail records at a large academic medical center showed that 912,386 calls were placed with a total duration of 23,186 hours. Approximately half of all calling called number pairs had an average call duration under 60 seconds and of these the average call duration was 27 seconds.

Conclusions: Cross-correlation of phone calls identified by clinical cost center can be used to generate graphical displays of clinical enterprise communications. Many calls are short. The compact data transfers within short calls may serve as automation or re-design targets. The large absolute amount of time medical center employees were engaged in VoIP telecommunications suggests that analysis of telephone call patterns may offer additional insights into core clinical workflows.

Citation: Rucker DW. Using telephony data to facilitate discovery of clinical workflows. Appl Clin Inform 2017; 8: 381–395 https://doi.org/10.4338/ACI-2016-11-RA-0191

Ethical approval

“All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.”


 
  • References

  • 1 Unertl KM, Novak LL, Johnson KB, Lorenzi NM. Traversing the many paths of workflow research: developing a conceptual framework of workflow terminology through a systematic literature review. Journal of the American Medical Informatics Association: JAMIA 2010; 17 (03) 265-273.
  • 2 Walsh C, Siegler EL, Cheston E, O’Donnell H, Collins S, Stein D, Vawdrey DK, Stetson PD. Provider-to-provider electronic communication in the era of meaningful use: a review of the evidence. Journal of Hospital Medicine 2013; 8 (10) 589-597.
  • 3 Westbrook JI, Ampt A, Williamson M, Nguyen K, Kearney L. Methods for measuring the impact of health information technologies on clinicians’ patterns of work and communication. Studies in Health Technology and Informatics 2007; 129 Pt 2 1083-1087.
  • 4 Hayes GR, Lee CP, Dourish P. Organizational routines, innovation, and flexibility: the application of narrative networks to dynamic workflow. International Journal of Medical Informatics 2011; 80 (08) e161-e177.
  • 5 Wu RC, Lo V, Morra D, Wong BM, Sargeant R, Locke K, Cavalcanti R, Quan SD, Rossos P, Tran K, Cheung M. The intended and unintended consequences of communication systems on general internal medicine inpatient care delivery: a prospective observational case study of five teaching hospitals. Journal of the American Medical Informatics Association: JAMIA 2013; 20 (Suppl. 04) 766-777.
  • 6 Unertl KM, Weinger MB, Johnson KB, Lorenzi NM. Describing and modeling workflow and information flow in chronic disease care. Journal of the American Medical Informatics Association: JAMIA 2009; 16 (06) 826-836.
  • 7 Lopetegui M, Yen PY, Lai A, Jeffries J, Embi P, Payne P. Time motion studies in healthcare: what are we talking about?. Journal of Biomedical Informatics 2014; 49: 292-299.
  • 8 Hefter Y, Madahar P, Eisen LA, Gong MN. A Time-Motion Study of ICU Workflow and the Impact of Strain. Critical Care Medicine 2016; 44 (08) 1482-1489.
  • 9 Leafloor CW, Lochnan HA, Code C, Keely EJ, Rothwell DM, Forster AJ, Huang AR. Time-motion studies of internal medicine residents’ duty hours: a systematic review and meta-analysis. Advances in Medical Education and Practice 2015; 6: 621-629.
  • 10 Zheng K, Haftel HM, Hirschl RB, O’Reilly M, Hanauer DA. Quantifying the impact of health IT implementations on clinical workflow: a new methodological perspective. Journal of the American Medical Informatics Association: JAMIA 2010; 17 (04) 454-461.
  • 11 Westbrook JI, Li L, Georgiou A, Paoloni R, Cullen J. Impact of an electronic medication management system on hospital doctors’ and nurses’ work: a controlled pre-post, time and motion study. Journal of the American Medical Informatics Association: JAMIA. 2013; 20 (06) 1150-1158.
  • 12 Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions. Journal of the American Medical Informatics Association: JAMIA 2011; 18 (02) 112-117.
  • 13 Huang Z, Dong W, Ji L, Gan C, Lu X, Duan H. Discovery of clinical pathway patterns from event logs using probabilistic topic models. Journal of Biomedical Informatics 2014; 47: 39-57.
  • 14 Huang Z, Lu X, Duan H, Fan W. Summarizing clinical pathways from event logs. Journal of Biomedical Informatics 2013; 46 (01) 111-127.
  • 15 Hribar MR, Read-Brown S, Reznick L, Lombardi L, Parikh M, Yackel TR, Chiang MF. Secondary Use of EHR Timestamp data: Validation and Application for Workflow Optimization. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium 2015; 2015: 1909-1917.
  • 16 Chassin MR. Improving the quality of health care: what‘s taking so long?. Health Affairs (Project Hope) 2013; 32 (10) 1761-1765.
  • 17 Grando MA, Peleg M, Cuggia M, Glasspool D. Patterns for collaborative work in health care teams. Artificial Intelligence in Medicine 2011; 53 (03) 139-160.
  • 18 Gooch P, Roudsari A. Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. Journal of the American Medical Informatics Association: JAMIA 2011; 18 (06) 738-748.
  • 19 Chen Y, Lorenzi NM, Sandberg WS, Wolgast K, Malin BA. Identifying collaborative care teams through electronic medical record utilization patterns. Journal of the American Medical Informatics Association: JAMIA. 2016 Aug 28. https://doi.org/10.1093/jamia/ocw124
  • 20 Valdez RS, Ramly E, Brennan PF. Industrial and Systems Engineering and Health Care: Critical Areas of Research--Final Report. Rockville, MD: Agency for Healthcare Research and Quality; 2010. Contract No.: AHRQ Publication No. 10–0079.
  • 21 Westbrook JI, Braithwaite J, Georgiou A, Ampt A, Creswick N, Coiera E, Iedema R. Multimethod evaluation of information and communication technologies in health in the context of wicked problems and sociotechnical theory. Journal of the American Medical Informatics Association: JAMIA 2007; 14 (Suppl. 06) 746-755.
  • 22 Vankipuram M, Kahol K, Cohen T, Patel VL. Visualization and analysis of activities in critical care environments. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium 2009; 2009: 662-666.
  • 23 Chen Y, Xie W, Gunter CA, Liebovitz D, Mehrotra S, Zhang H, Malin B. Inferring Clinical Workflow Efficiency via Electronic Medical Record Utilization. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium 2015; 2015: 416-425.
  • 24 Partington A, Wynn M, Suriadi S, Ouyang C, Karnon J. Process mining for clinical processes: a comparative analysis of four Australian hospitals. ACM Transactions on Management Information Systems (TMIS) 2015; 5 (04) 19.
  • 25 Vawdrey DK, Wilcox LG, Collins S, Feiner S, Mamykina O, Stein DM, Bakken S, Fred MR, Stetson PD. Awareness of the Care Team in Electronic Health Records. Applied Clinical Informatics 2011; 2 (04) 395-405.
  • 26 Ahmed S, Manaf NH, Islam R. Effects of Lean Six Sigma application in healthcare services: a literature review. Reviews on Environmental Health 2013; 28 (04) 189-194.
  • 27 DelliFraine JL, Langabeer JR, 2nd Nembhard IM. Assessing the evidence of Six Sigma and Lean in the health care industry. Quality Management in Health Care 2010; 19 (03) 211-225.
  • 28 Pentland A, Choudhury T, Eagle N, Singh P. Human dynamics: computation for organizations. Pattern Recogn Lett 2005; 26 (04) 503-511.
  • 29 Lai HM, Lin IC, Tseng LT. High-level managers’ considerations for RFID adoption in hospitals: an empirical study in Taiwan. Journal of Medical Systems 2014; 38 (02) 3.
  • 30 Weibel N, Rick S, Emmenegger C, Ashfaq S, Calvitti A, Agha Z. LAB-IN-A-BOX: semi-automatic tracking of activity in the medical office. Pers Ubiquit Comput 2015; 19 (02) 317-334.
  • 31 Chen ES, Cimino JJ. Automated discovery of patient-specific clinician information needs using clinical information system log files. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium 2003: 145-149.
  • 32 Bouarfa L, Dankelman J. Workflow mining and outlier detection from clinical activity logs. Journal of Biomedical Informatics 2012; 45 (06) 1185-1190.
  • 33 Schnell K, Puri C, Mahler P, Dukatz C. Teaching an Old Log New Tricks with Machine Learning. Big Data 2014; 2 (01) 7-11.
  • 34 Wu RC, Tran K, Lo V, O’Leary KJ, Morra D, Quan SD, Perrier L. Effects of clinical communication interventions in hospitals: a systematic review of information and communication technology adoptions for improved communication between clinicians. International Journal of Medical Informatics 2012; 81 (11) 723-732.
  • 35 Serdarevic M, Fazzino TL, MacLean CD, Rose GL, Helzer JE. Recruiting 9126 Primary Care Patients by Telephone: Characteristics of Participants Reached on Landlines, Basic Cell Phones, and Smartphones. Population Health Management 2016; 19 (03) 212-215.
  • 36 Mendonca EA, Chen ES, Stetson PD, McKnight LK, Lei J, Cimino JJ. Approach to mobile information and communication for health care. International Journal of Medical Informatics 2004; 73 (7–8) 631-638.
  • 37 Williams J. Left to their own devices how healthcare organizations are tackling the BYOD trend. Biomedical Instrumentation & Technology/Association for the Advancement of Medical Instrumentation 2014; 48 (05) 327-339.
  • 38 Johnson AC, El Hajj SC, Perret JN, Caffery TS, Jones GN, Musso MW. Smartphones in medicine: emerging practices in an academic medical center. Journal of Medical Systems 2015; 39 (01) 164.
  • 39 Patel N, Siegler JE, Stromberg N, Ravitz N, Hanson CW. Perfect Storm of Inpatient Communication Needs and an Innovative Solution Utilizing Smartphones and Secured Messaging. Applied Clinical Informatics 2016; 7 (03) 777-789.
  • 40 Cronin RM, Davis SE, Shenson JA, Chen Q, Rosenbloom ST, Jackson GP. Growth of Secure Messaging Through a Patient Portal as a Form of Outpatient Interaction across Clinical Specialties. Applied Clinical Informatics 2015; 6 (02) 288-304.
  • 41 Whitlow ML, Drake E, Tullmann D, Hoke G, Barth D. Bringing technology to the bedside: using smart-phones to improve interprofessional communication. Computers, Informatics, Nursing: CIN 2014; 32 (07) 305-311.
  • 42 Scholl J, Groth K. Of organization, device and context: Interruptions from mobile communication in highly specialized care. Interacting with Computers 2012; 24 (05) 358-373.
  • 43 Hopkinson SG, Jennings BM. Interruptions during nurses’ work: A state-of-the-science review. Research in Nursing & Health 2013; 36 (01) 38-53.
  • 44 Cornell P, Riordan M, Townsend-Gervis M, Mobley R. Barriers to critical thinking: workflow interruptions and task switching among nurses. The Journal of Nursing Administration 2011; 41 (10) 407-414.
  • 45 Brixey JJ, Robinson DJ, Turley JP, Zhang J. The roles of MDs and RNs as initiators and recipients of interruptions in workflow. International Journal of Medical Informatics 2010; 79 (06) E109-E115.
  • 46 Patterson ES, Wears RL. Patient handoffs: standardized and reliable measurement tools remain elusive. Joint Commission Journal on Quality and Patient safety/Joint Commission Resources 2010; 36 (02) 52-61.
  • 47 Bowen M, Prater A, Safdar NM, Dehkharghani S, Fountain JA. Utilization of Workflow Process Maps to Analyze Gaps in Critical Event Notification at a Large, Urban Hospital. Journal of Digital Imaging 2016; 29 (04) 420-424.
  • 48 Weigl M, Muller A, Angerer P, Hoffmann F. Workflow interruptions and mental workload in hospital pediatricians: an observational study. BMC Health Services Research 2014; 14: 433.
  • 49 Vaisman A, Wu RC. Analysis of Smartphone Interruptions on Academic General Internal Medicine Wards. Frequent Interruptions may cause a 舘Crisis Mode‘ Work Climate. Applied Clinical Informatics 2017; 8 (01) 1-11.
  • 50 Poshywak J. Fighting infection with RTLS. Health Management Technology 2015; 36 (02) 16.
  • 51 Samal L, Stavroudis T, Miller R, Lehmann H, Lehmann C. Effect of a laboratory result pager on provider behavior in a neonatal intensive care unit. Applied clinical Informatics 2011; 2 (03) 384-394.
  • 52 Steitz BD, Weinberg ST, Danciu I, Unertl KM. Managing and Communicating Operational Workflow: Designing and Implementing an Electronic Outpatient Whiteboard. Applied Clinical Informatics 2016; 7 (01) 59-68.
  • 53 Johnson W. Voice over IP: how computing technology is being used in mobile communications. Journal of healthcare information management: JHIM 2005; 19 (04) 24-31.
  • 54 Flanagan WA. VoIP and Unified Communications. Hoboken, NJ: John Wiley and Sons; 2012
  • 55 Werbach K. Using VoIP to compete. Harvard Business Review 2005; 83 (09) 2005; 83(9): 140-147 , 160.
  • 56 Cisco Unified Communications Manager Call Detail Records Administration Guide. Release 8.6(1) ed. San Jose, CA: Cisco Systems; 2011
  • 57 Finke J, Hartmann D. Implementing Cisco Unified Communications Manager, Part 1 (CIPT) Foundation Learning Guide. Indianapolis, IN: Cisco Press; 2012
  • 58 Christiansen T, Foy BD, Wall L. Programming Perl. 4th ed. Sebastopol, CA: O’Reilly; 2012. xli, 1130 p. p.
  • 59 De Meo P, Ferrara E, Fuimara G, Provetti A. On Facebook Most Ties Are Weak. Communications of the ACM 2014; 57 (11) 78-84.
  • 60 Jiang ZQ, Xie WJ, Li MX, Podobnik B, Zhou WX, Stanley HE. Calling patterns in human communication dynamics. Proceedings of the National Academy of Sciences of the United States of America 2013; 110 (05) 1600-1605.
  • 61 Onnela JP, Saramaki J, Hyvonen J, Szabo G, de Menezes MA, Kaski K, Barabasi AL, Kertesz J. Analysis of a large-scale weighted network of one-to-one human communication. New J Phys. 2007: 9.
  • 62 Zhang P, Serban N. Discovery, visualization and performance analysis of enterprise workflow. Computational Statistics & Data Analysis 2007; 51 (05) 2670-2687.
  • 63 Dong W, Lepri B, Pentland A. Modeling the co-evolution of behaviors and social relationships using mobile phone data. Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia; Beijing, China. 2107613: ACM 2011: 134-143.
  • 64 Cebrian M, Pentland A, Kirkpatrick S. Disentangling Social Networks Inferred from Call Logs. Proceedings of CoRR 2010: 1-6.
  • 65 Candia J, Gonzalez MC, Wang P, Schoenharl T, Madey G, Barabasi AL. Uncovering individual and collective human dynamics from mobile phone records. J Phys a-Math Theor. 2008 41(22). iopscience.iop.org/1751–8113/41/22/224015
  • 66 Barzel B, Sharma A, Barabási A-L. Chapter 9 –Graph Theory Properties of Cellular Networks. In: Dekker AJMWV. editor. Handbook of Systems Biology. San Diego: Academic Press; 2013: 177-177.
  • 67 Merrill J, Hripcsak G. Using social network analysis within a department of biomedical informatics to induce a discussion of academic communities of practice. Journal of the American Medical Informatics Association: JAMIA 2008; 15 (06) 780-782.
  • 68 Fortunato S. Community detection in graphs. Physics Reports 2010; 486 (3–5) 75-174.
  • 69 Newman MEJ. Networks –An Introduction. Oxford: 2010
  • 70 Leskovec J, Rajaraman A, Ullman JD. Mining of Massive Datasets. Palo Alto, CA: Cambridge University Press; 2015
  • 71 Bastian M, Heymann S, Jacomy M. Gephi: An Open Source Software for Exploring and Manipulating Networks. 2009
  • 72 Safran C, Jones PC, Rind D, Bush B, Cytryn KN, Patel VL. Electronic communication and collaboration in a health care practice. Artificial Intelligence in Medicine 1998; 12 (02) 137-151.
  • 73 Balint BJ, Steenburg SD, Lin H, Shen C, Steele JL, Gunderman RB. Do telephone call interruptions have an impact on radiology resident diagnostic accuracy?. Academic Radiology 2014; 21 (12) 1623-1628.
  • 74 Iversen TB, Melby L, Toussaint P. Instant messaging at the hospital: supporting articulation work?. International Journal of Medical Informatics 2013; 82 (09) 753-761.
  • 75 Richardson JE, Ash JS. The effects of hands-free communication device systems: communication changes in hospital organizations. Journal of the American Medical Informatics Association: JAMIA 2010; 17 (01) 91-98.
  • 76 Shah DR, Galante JM, Bold RJ, Canter RJ, Martinez SR. Text messaging among residents and faculty in a university general surgery residency program: prevalence, purpose, and patient care. Journal of Surgical Education 2013; 70 (06) 826-834.
  • 77 Anderson C. The Long Tail: Why the future of business is selling less of more. New York: Hyperion Books; 2006