quality improvement decision support for quality improvement lecture a this material (comp12_unit5a)...

Quality Improvement

Decision Support for Quality Improvement

Lecture a

This material (Comp12_Unit5a) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000013.


Learning Objectives─Lecture a

• Define decision support, its importance, and why it is difficult to implement.

• Compare decision support tools that help improve quality.

2Health IT Workforce Curriculum Version 3.0/Spring 2012

Quality Improvement Decision Support for Quality

Improvement Lecture a

Clinical Decision Support Definitions

• Clinical Decision Support: “… a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve health and healthcare delivery.” (Improving outcomes with clinical decision support: an implementer’s guide. Second Edition. HIMSS, 2011)

• Clinical Decision Support Systems: “…active knowledge systems which use two or more items of patient data to generate case-specific advice.” (Wyatt, J. & Spiegelhalter, D.,1991)




Examples of CDS

• Hospital example: appropriate dosing of antibiotics

• Primary Care example: compliance with preventive recommendations




CDS Five Rights Model

• The right information

• To the right person

• In the right CDS intervention format

• Through the right channel

• At the right time in workflow




Types of CDS

• Relevant data displays • Smart documentation forms • Order facilitators (order sets, order consequents,

order modifiers) • Extended-time guideline & protocol followers • Targeted reference, including contextually

relevant medical references or info buttons • Reactive alerts




Types of CDS

• Task assistants for tasks such as drug dosing and acknowledging laboratory results

• Diagnostic suggestions • Patient summaries for clinician hand-offs• Procedure refreshers, training, and reminders • Performance dashboards with prompts for areas needing

attention • Tracking and management systems that facilitate task

prioritization and whole-service management




Clinical Decision Support SystemsEffects on Medication Safety

• CDSS combined with CPOE can improve medication safety & reduce medication-related expenditures– Introduces automation at the time of ordering– Increases legibility– Assures that the order is safe and compliant with guidelines

Seidling and colleagues (2010) created a comprehensive software-algorithm that extracted relevant patient information —age, renal function, co-medications — and adjusted upper dose limits to these patient characteristics. This highly specific algorithm-based CDSS significantly improved electronic prescription quality & reduced prescription of excessive doses.




Clinical Decision Support Systems—Are They Being Used?

• Despite potential usefulness, lack of widespread acceptance

• “Diagnosis is the dominant decision-making issue in medicine.”

• “Clinicians will use knowledge-based systems if the programs can be shown to function at the level of experts.”

• “Clinicians will use stand-alone decision-support tools.” (Edward (Ted) E. Shortliffe, Conference on Medical Thinking University College, London, June 23, 2006)




Myths

Decision Support Key Functions




Decision SupportAdministrative Function

• Supports clinical coding and documentation

• Example: authorization of procedures and referrals

• “Choosing diagnosis codes is a non-intuitive operation for the practitioner. Mistakes are frequent with severe consequences on healthcare evaluation and funding” (Peters, 2005)




Decision SupportComplexity Management Function

• Assists with the details of managing clinical complexity

• Examples: – Keeping patients on research and

chemotherapy protocols– Tracking orders– Referral follow-up– Preventive care




Decision SupportCost Control Function

• Supports control of costs

• Examples: – Monitoring medication orders– Avoiding duplicate or unnecessary tests




Decision SupportDecision Support Function

• Supports clinical diagnosis and treatment plan processes and promotes use of best practices

• Examples:– Condition-specific clinical practice guidelines– Population-based management– Clinical calculation– Disease registries and patient tracking tools– Summary screens– Order sets (Metzger, J. and Macdonald, K. (2002))




Unintended Consequences of CDS

• Content– Elimination or changing of roles of clinicians and

staff– Currency of CDS content– Wrong or misleading CDS content

• Presentation– Rigidity of systems– Alert fatigue– Sources of potential error





Summary—Lecture a

• CDSS integrate a medical knowledge base, patient data, and an inference engine to generate care-specific advice.

• Despite potential usefulness, there has not been widespread clinician acceptance of CDSS

• The design of useful CDS must include: right information, right person, right CDS intervention format, right channel and right time in workflow..

• Key functions of CDSS are: administrative, managing clinical complexity/details, cost control, and decision support.





References—Lecture aReferences• Ash, J.S., Sittig, D.F., Campbell, E.M, et al.. Some unintended consequences of clinical decision support systems. AMIA 2007 Symposium Proceedings. 11:26-30. 2007 • Bates, D. Clinical Decision Support Workshop, ONC, August 25-26, 2009• Chaffee, B.W. Future of clinical decision support in computerized prescriber order entry. American Journal of Health System Pharmacists. 67: 932-935. 2010. • De Clercq, P.A., Blom, J.A., Hasman, A., Korsten, H.H.M. A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques. Journal of Clinical Monitoring. 15:109-117. 1999. • Handler, J,A., Feied, C.F., Coonan ,K., et al. Computerized physician order entry and online decision support. Academy of Emergency Medicine. 11(11):1135-1141. 2004. • Kawamoto, K., Houlihan, C.A., Balas, E.A., Lobach, D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 330(7494):765. 2005.• Kuperman, G.J., Bobb, A., Payne, T.H., et al. Medication-related clinical decision-support in computerized provider order entry systems: a review. Journal of the American Medical Informatics Association. 14(1), 29-40. 2007.• Metzger, J. and Macdonald, K. (2002), Clinical decision support for the independent physician practice. Health Reports, California Health Care Foundation. 2002• Moxey, A., Robertson, J., Newby, D., Hains, I., et al. Computerized clinical decision support for prescribing: provision does not guarantee uptake. Journal of the American Medical Informatics Association. 17:25-33. 2001.• Osheroff ,J.A. Improving medication use and outcomes with clinical decision support: a step-by-step guide. Chicago, IL: The Healthcare Information and Management Systems Society. 2009.





References—Lecture aReferences• Peters, J. & Le Cornu, R. (2005). ‘Beyond Communities of Practice: Learning Circles for Transformational School

Leadership’, Chapter 6 in P. Carden & T. Stehlik (eds) Beyond Communities of Practice, Queensland, Post Pressed.

• Seidling ,H.M., Schmitt, S.P.W., Bruckner ,T., et al. Patient-specific electronic decision support reduces prescription of excessive doses. Quality and Safety in Health Care. 2010 April 27.

• Shortliffe, E.E. Conference on Medical Thinking University College, London, 2006 June 23.• Wyatt, J., Spiegelhalter, D. Field trials of medical decision-aids: potential problems and solutions. Proceedings of

the Annual Symposium on Computer Application in Medical Care. 1991; 3-7




Images

Slide 10: Decision Support. Adapted from Perreault & Metzer 1999 by Dr. Anna Maria Izquierdo-Porrera

quality improvement decision support for quality improvement lecture a this material (comp12_unit5a)...

Documents

decision support tools

workforce curriculum

department of health

health information technology

healthrelated decisions

clinical knowledge

order consequents

patient characteristics