Unmet information needs of clinical teams delivering care to complex patients and design strategies to address those needs

OBJECTIVES To identify the unmet information needs of clinical teams delivering care to patients with complex medical, social, and economic needs; and to propose principles for redesigning electronic health records (EHR) to address these needs. MATERIALS AND METHODS In this observational study, we interviewed and observed care teams in 9 community health centers in Oregon and Washington to understand their use of the EHR when caring for patients with complex medical and socioeconomic needs. Data were analyzed using a comparative approach to identify EHR users' information needs, which were then used to produce EHR design principles. RESULTS Analyses of > 300 hours of observations and 51 interviews identified 4 major categories of information needs related to: consistency of social determinants of health (SDH) documentation; SDH information prioritization and changes to this prioritization; initiation and follow-up of community resource referrals; and timely communication of SDH information. Within these categories were 10 unmet information needs to be addressed by EHR designers. We propose the following EHR design principles to address these needs: enhance the flexibility of EHR documentation workflows; expand the ability to exchange information within teams and between systems; balance innovation and standardization of health information technology systems; organize and simplify information displays; and prioritize and reduce information. CONCLUSION Developing EHR tools that are simple, accessible, easy to use, and able to be updated by a range of professionals is critical. The identified information needs and design principles should inform developers and implementers working in community health centers and other settings where complex patients receive care.

[1]  Tiffany C. Veinot,et al.  Physicians' perceptions of the impact of the EHR on the collection and retrieval of psychosocial information in outpatient diabetes care , 2018, Int. J. Medical Informatics.

[2]  Corinne M. Graffunder,et al.  The National Prevention Strategy: leveraging multiple sectors to improve population health. , 2015, American journal of public health.

[3]  James Beebe,et al.  Rapid Assessment Process: An Introduction , 2001 .

[4]  Richard W. Grant,et al.  Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network , 2015, Journal of General Internal Medicine.

[5]  Jessica S. Ancker,et al.  Evaluating health information technology in community-based settings: lessons learned , 2011, J. Am. Medical Informatics Assoc..

[6]  Ferdinand T. Velasco,et al.  Improving Outcomes with Clinical Decision Support: An Implementer's Guide , 2012 .

[7]  Rachel Gold,et al.  Developing Electronic Health Record (EHR) Strategies Related to Health Center Patients' Social Determinants of Health , 2017, The Journal of the American Board of Family Medicine.

[8]  Amanda von Taube,et al.  Closing the loop with an enhanced referral management system , 2015, AMIA.

[9]  Regina M. Benjamin,et al.  The National Prevention Strategy: Shifting the Nation's Health-Care System , 2011, Public health reports.

[10]  Michelle L. Rogers,et al.  Use of a human factors approach to uncover informatics needs of nurses in documentation of care , 2013, Int. J. Medical Informatics.

[11]  Jeroan J. Allison,et al.  Patient Complexity: More Than Comorbidity. The Vector Model of Complexity , 2007, Journal of General Internal Medicine.

[12]  Tosha B. Wetterneck,et al.  Technology Evaluation: Workarounds to Barcode Medication Administration Systems: Their Occurrences, Causes, and Threats to Patient Safety , 2008, J. Am. Medical Informatics Assoc..

[13]  Ben-Tzion Karsh,et al.  A typology of electronic health record workarounds in small-to-medium size primary care practices. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[14]  Emily S. Patterson,et al.  Workarounds to Intended Use of Health Information Technology: A Narrative Review of the Human Factors Engineering Literature , 2018, Hum. Factors.

[15]  Bonnie J Wakefield,et al.  Rework and workarounds in nurse medication administration process: Implications for work processes and patient safety , 2010, Health care management review.

[16]  Cosmin Adrian Bejan,et al.  Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records , 2018, J. Am. Medical Informatics Assoc..

[17]  Tiffany C. Veinot,et al.  Psychosocial information use for clinical decisions in diabetes care , 2019, J. Am. Medical Informatics Assoc..

[18]  K C Stange,et al.  Understanding practice from the ground up. , 2001, The Journal of family practice.

[19]  Tiffany C. Veinot,et al.  Technical infrastructure implications of the patient work framework , 2015, J. Am. Medical Informatics Assoc..

[20]  Karin E. Johnson,et al.  A Conceptual Model of the Role of Complexity in the Care of Patients With Multiple Chronic Conditions , 2014, Medical care.

[21]  Elizabeth A. Bayliss,et al.  Primary Care Physician Insights Into a Typology of the Complex Patient in Primary Care , 2015, The Annals of Family Medicine.

[22]  D. Cohen,et al.  Evaluative Criteria for Qualitative Research in Health Care: Controversies and Recommendations , 2008, The Annals of Family Medicine.

[23]  Brad Wright,et al.  Who governs federally qualified health centers? , 2013, Journal of health politics, policy and law.

[24]  A. Goddu,et al.  Food Rx: A Community–University Partnership to Prescribe Healthy Eating on the South Side of Chicago , 2015, Journal of prevention & intervention in the community.

[25]  Rachel Gold,et al.  "Community vital signs": incorporating geocoded social determinants into electronic records to promote patient and population health , 2016, J. Am. Medical Informatics Assoc..

[26]  Robert L. Wears,et al.  Health information technology: fallacies and sober realities , 2010, J. Am. Medical Informatics Assoc..

[27]  Megan J. Hoopes,et al.  Following Uninsured Patients Through Medicaid Expansion: Ambulatory Care Use and Diagnosed Conditions , 2019, The Annals of Family Medicine.

[28]  Avi Parush,et al.  The EHR and building the patient's story: A qualitative investigation of how EHR use obstructs a vital clinical activity , 2015, Int. J. Medical Informatics.

[29]  Jiajie Zhang,et al.  TURF: Toward a unified framework of EHR usability , 2011, J. Biomed. Informatics.

[30]  Jakob Nielsen,et al.  Usability engineering , 1997, The Computer Science and Engineering Handbook.

[31]  Dean F. Sittig,et al.  Rapid Assessment of Clinical Information Systems in the Healthcare Setting , 2010, Methods of Information in Medicine.

[32]  Melissa D. Klein,et al.  Identifying Social Risk via a Clinical Social History Embedded in the Electronic Health Record , 2012, Clinical pediatrics.

[33]  Shaun J. Grannis,et al.  Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services , 2018, J. Am. Medical Informatics Assoc..

[34]  Paul N. Gorman,et al.  Information needs of physicians , 1995 .

[35]  P. Carayon,et al.  SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients , 2013, Ergonomics.

[36]  Jessica S. Ancker,et al.  Health information exchange in the wild: the association between organizational capability and perceived utility of clinical event notifications in ambulatory and community care , 2017, J. Am. Medical Informatics Assoc..

[37]  Michael N Cantor,et al.  Integrating Data On Social Determinants Of Health Into Electronic Health Records. , 2018, Health affairs.

[38]  Ben Shneiderman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[39]  K D Clark,et al.  Translating Research into Agile Development (TRIAD): Development of Electronic Health Record Tools for Primary Care Settings , 2019, Methods of Information in Medicine.