Commentary
QUALITY IMPROVEMENT STRATEGIES FOR CRITICAL CARE NURSING By Amanda B. Barnhorst, MD, MHA, Mirian Martinez, RN, PCC, and Hayley B. Gershengorn, MD
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vidence-based practice and performance improvement have become familiar terms in developed health care settings over the past 2 decades. The major instigator for better quality health care was the Institute of Medicine’s (IOM’s) report in 2000, To Err is Human,1 proposing that medical errors are one of the leading causes of death in the United States. As a result, large organizations, both private and governmental, now encourage evidence-based care by setting specific quality and safety initiatives for health care providers to follow. Although cooperation is not always mandatory, failure to collect data and/or comply with these initiatives can result in loss in reimbursement.2 Outside these “mandated” initiatives, providers in individual hospitals and units—including the intensive care unit (ICU)—are also encouraged to identify measures important to their specific situations. Improving care in the ICU should be a goal of every provider in the unit. Critically ill patients undergo extensive invasive testing, may have several monitoring devices, and can be receiving multiple medications. This situation often places them at a greater risk for potentially preventable conditions that can be associated with high morbidity and mortality such as iatrogenic injuries, hospital–acquired infections, pressure ulcers, and delirium.3 The nursing profession currently comprises the largest segment of the nation’s health care workforce4 and generally has the most immediate and most consistent patient contact. Critical care nurses often perform most of the care, patient assessments, and evaluations in the ICU, which places them in the perfect position to © 2015 American Association of Critical-Care Nurses doi: http://dx.doi.org/10.4037/ajcc2015104 www.ajcconline.org
identify, initiate, evaluate, and sustain quality initiatives. Another IOM report, The Future of Nursing: Leading Change, Advancing Health stresses nurses’ fundamental role in transforming our health care system so that it “provides seamless, affordable, quality care that is accessible to all and leads to improved health outcomes.”4 This paper will provide guidance in choosing appropriate measures for quality initiatives in an ICU setting, assuring accurate data collection, providing meaningful results, and implementing change. Specific focus will be placed on how nurses can partake in and lead these initiatives.
Choose the Right Metric One of the first steps in developing a quality initiative is deciding what to measure. The metric must both accurately gauge the quality of care and be reliably measurable. Concrete outcomes such as mortality are common metrics used for quality initiatives but these metrics may not always be appropriate for initiatives in the ICU.5,6 For example, mortality in a patient with a severe stroke progressing to brain death does not necessarily give an accurate measure of quality of care. A more appropriate metric in this case may include use of end-of-life care such as palliative care or organ donation. Metrics should be relevant to the patient, those caring for the patient, and the health care organization. An example of one metric currently measured in the ICU that fulfills the above criteria is catheter-related urinary tract infections (CAUTIs). This metric is tracked and reported because CAUTIs are one of the most common health care–associated infections and are associated with increased morbidity, mortality, hospital cost, and length of stay.7 This measure is relevant to both the patient and the organization because decreases in CAUTI rates may improve
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Quality metrics can be divided into 3 broad categories: structural, process, and clinical or health outcomes.
patient outcomes as well as potentially decrease health care costs. First described by Donabedian in 19668 and recently applied to surgical outcomes by Birkmeyer and colleagues,9 quality metrics can be divided into 3 broad categories: structural, process, and clinical or health outcomes. Structural measures reflect the system or setting in which the health care is delivered. Examples of structural measures in a critical care setting include the level of training for ICU staff and physicians, staffing models, and open versus closed ICUs. Although they are not direct measures of care quality, such structural differences can be associated with high- or low-quality care.10-12 For example, studies have shown that patients in hospitals with better critical care nurse work environments and higher proportions of critical care nurses with a bachelor's degree in nursing experienced significantly lower odds of death.13 Structural measures, such as nursing education, are often fairly easy to quantify. But, for some hospitals, the association between a structural measure and high quality care may not be obvious; moreover, financial and staff constraints may render collection of some structural data arduous. The second category, process measures, describes the care that the patient actually receives. These measures evaluate adherence to various protocols and can be linked to outcomes. They are often used to compare one unit or hospital with another and are usually based on best available evidence such as randomized control trials and systematic reviews.14 Initiatives to improve CAUTI rates are examples of initiatives that often employ measurement of process metrics; such initiatives are a focus of quality improvement programs because decreasing CAUTI
About the Authors Amanda B. Barnhorst is chief critical care fellow, Mirian Martinez is a research nurse and quality assurance nurse, and Hayley B. Gershengorn is an assistant professor at the Jay B. Langner Critical Care System, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York. Corresponding author: Amanda B. Barnhorst, MD, MHA, Division of Critical Care Medicine, Montefiore Medical Center, 111 E 210th St, Gold Zone, Main Floor, Bronx, NY 10467. (E-mail: [email protected]).
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rates has a positive impact on outcome.7 The Centers for Disease Control and Prevention’s CAUTI guidelines cite multiple studies that have identified specific interventions that may reduce CAUTI rates.7 These include catheter insertion only for appropriate indications, maintenance of catheters only as long as needed, insertion by properly trained persons using aseptic technique and sterile equipment, and maintenance of unobstructed urine flow.7 There are difficulties with initiatives based on the measurement of process metrics, however. First, what is considered best practice today may not be considered best practice tomorrow due to the evolution of research. Second, having best-practice processes of care does not necessarily mean that the actual care delivered is of high quality (eg, the use of proper urinary catheter technique may not assure low CAUTI rates). The last category is health or clinical outcome measures. The National Quality Measures Clearinghouse, a public resource for evidence-based quality measures and measure sets, defines a health or clinical outcome as “a health state of a patient resulting from health care.”15 Examples of these measures include mortality rates, complication rates, length of stay, readmission rates, patient satisfaction, functional health status, and other measures of health-related quality of life. Using the example of CAUTIs, the comparison of CAUTI rates among units and hospitals is an example of a clinical or health outcome. These rates are often publically reported and can be helpful when patients are choosing a specific health care entity such as a nursing home or surgeon.16 Caution must be used when interpreting these rates, however, because rates calculated for hospitals, units, or physicians with limited numbers of patients can be grossly inaccurate.17,18 Birkmeyer and colleagues9 discuss a paradigm to help choose metrics based on the risk and the frequency of the procedure being studied. They propose that high-risk, low frequency procedures should be evaluated by structure based measures; low-risk, high-frequency procedures should be evaluated using process measures; and high risk, high-frequency procedures should be evaluated by outcome measures. Gershengorn and colleagues19 introduce a flow diagram to help select metrics
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When planning for data collection, it is important to consider who will be capturing the data and how data collection will affect their workload.
”
1. 2. 3. 4.
Is it: Identifiable? Measureable? Relevant? Timely?
yes
Measure metric
Is quality/ performance acceptable?
yes
no no
yes
Process 1. 2. 3. 4.
Choose process metric (eg, % of time right antibiotic is chosen)
Is it: Identifiable? Measureable? Relevant? Timely?
yes
Measure metric
Is quality/ performance acceptable?
no
no no yes
Structure 1. 2. 3. 4.
Choose structure metric (eg, availability of resources to look-up right antibiotics)
Is it: Identifiable? Measureable? Relevant? Timely?
yes
Measure metric
Is quality/ performance acceptable?
no
Initiate programs to improve on chosen metric
Select an event to evaluate (eg, administration of early, appropriate antibiotics)
Choose outcome metric (eg, % of time antibiotic given as desired)
Choose a new project
Outcome
no
Figure Algorithm for appropriate metric identification. Reprinted with permission of the American Thoracic Society. Copyright ©2014 American Thoracic Society. Official Journal of the American Thoracic Society.19
starting with outcome and progressing to process and then structure until a good starting metric is identified (see Figure). A key component to metric selection, however, is interaction with frontline clinicians who know the structures and processes of care best. Including these providers (eg, the bedside nurse, the clinical pharmacist, the physical/occupational therapist, the respiratory therapist) in metric selection at the beginning will not only improve on the data collection but will ensure buy-in during initiative implementation.
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Optimize Data Collection Once the measures are chosen, the next step is to determine how to accurately capture and record the data. Historically, patient data was written on paper and abstraction consisted of reading notes handwritten by health care providers. A chart abstractor would read the charts, decipher the handwriting, and attempt to pull out relevant data to be entered in a separate database. This form of data collection is resource intensive and often fraught with error. Wager and colleagues20 evaluated the accuracy and timeliness of physiologic
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Table Methods for data display Chart Type
Definition
Ideal use
Run charts Control charts
Display observed data in an ordered sequence
Easily display trends, shifts, and stability of data
Pareto charts
Individual values are plotted in descending order on the x axis; line graph plots cumulative frequency
Focuses the reader’s eye on the factors that have the greatest impact and where an intervention is likely to produce the greatest benefit
Waterfall charts Bar chart that displays Helps target both positive both positively and negand negative influences on atively influencing varia product and easily display ables and their impact the magnitude of each on the cumulative effect Scorecards and dashboards
Displays many different indictors together
Can be used to compare individual data with benchmarks. Are often are brightly colored and target areas of improvement.
data entry by technicians and nurses before and after electronic health record (EHR) implementation at a large tertiary hospital. They found that when using paper charting, the error rate approached 17% but when transitioned to data entry using an easily assessable mobile device, error rates fell to < 6%.20 With the introduction of EHRs, data can be captured in real time at the bedside and either automatically entered into a database or manually entered into a computer using an interface.21 Bedside data includes variables such physiologic data points, medication administration records, and assessments such as pain or Richmond Agitation-Sedation Scale.22 If this data is entered and saved using defined formats, it generally can be easily mined and analyzed. For example, when capturing a time of medication administration, if it is entered into a field defined and formatted as a standard unit of time (eg, mm/dd/yyyy or hh:mm), mining, analyzing, and displaying this data can be automated. However, if data is entered into an EHR using free text (daily progress or shift notes); searching for data is more difficult.23 The use of EHRs for data collection may eliminate data transcription errors,24-27 but may introduce new types of data entry inaccuracies. For example, equipment malfunction may result in erroneous data—some patient monitoring systems will automatically record all blood pressures taken even if the cuff is not connected to the patient, and this results in the collection of an inaccurate measurement. Additionally, the use of EHRs does not replace the need to be clear and precise about what data is to be collected. For example, “time zero” for sepsis patients means very different
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things to different individuals. For the emergency department nurse, this is often the time that the patient arrives at triage. For the ICU nurse, this may mean when the patient arrives to the ICU. No matter how the data is collected, without clarity on this issue, all data will be unreliable. Although the implementation of EHRs has been an important step towards consistent and quality health care, EHRs have certainly created additional burdens on providers.28 Many providers complain that the EHR has taken away face time with patients and can often produce redundancy.29 Some institutions maintain mixed electronic and paper medical records due to the costs of implementing the EHR.30 Other systems have not yet maximized interoperability (connections between different software applications that make it possible for unaffiliated providers to directly communicate).31-35 These setups can produce not only redundancy and data errors, but also contempt among those entering duplicate data. When planning for data collection, therefore, it is important to consider who will be capturing the data and how data collection will affect their workload. For example, bedside nurses or technicians are already capturing many data points. In fact, some studies have shown nurses spend nearly 30% of their day documenting while only 7% of their time is spent assessing the patients.36 Studies have also shown an association between better patient outcomes and more nursing time per patient-day.37 Therefore, adding additional documentation time to already overloaded nurses can potentially have a negative impact on patient care. An alternative approach may be to utilize current technology to automate data transfer from patient devices such as patient monitors, smart beds, and infusion pumps.38 A 2003 study indicated that ICU nurses can save up to 30% of documentation time when physiologic data is automated.39 As the nurse is often the provider most aware of all that is happening with and for a patient, he or she may be the best-positioned person to advise on how data collection can be streamlined into usual care activities.
Disseminate Data Once baseline data is collected and analyzed, the next step is dissemination to the team(s) involved. This step is important to keep the team engaged40 and can often be best accomplished by champions from different disciplines who have been involved in the project from its inception. Using charts and visual effects will strategically draw the viewer’s eye to the significant results. Ensure that values are clearly labeled on charts and legends are provided. The Table
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gives examples of various types of charts and their ideal usage. Finally, the method for public presentation of the data must be determined. In some settings, displays of unit-wide results in full view in patient-care areas may be appropriate whereas for others, oneon-one conversations about results with providers affected by the initiative may prove best. Many factors will go into shaping this decision including the unit’s culture, the type of initiative, and the general public’s interest in the results. Although not always practical, more public displays can engender friendly competition among units and increase enthusiasm for initiatives with positive results being a source of pride for participants.
Empower Nurses Because they make up the largest segment of the health care workforce4 and are on the frontline, nurses are well positioned to impact great change. Many critical care nurses only perform clinical duties in a single unit, thus they identify this unit as “home.” These nurses often feel motivated and empowered to improve care when possible. According to the 2004 IOM report, Keeping Patients Safe: Transforming the Work Environment of Nurses, “how well we are cared for by nurses affects our health, and sometimes can be a matter of life or death.”41 The IOM’s 2010 report addressing the future of nursing calls for nurses to be “full partners, with physicians and other health care professionals, in redesigning health care in the United States.”4 When leading quality initiatives, nurses have and will continue to play integral roles. For example, the recent randomized controlled trial evaluating checklists to decrease central–line associated blood stream infections (CLABSIs), was initiated and led by nursing.42 In this study, central venous catheters were placed in the intervention group using an infection prevention bundle previously described by Berenholtz et al.43 The intervention group was found to have a 70% reduction in mean number of CLABSIs by the end of phase 1 compared to 21% in the control group.42 Other studies striving to improve outcomes in preterm birth, enteral nutrition, and ventilator-associated pneumonia rates were all nurse-driven and had significant positive results.44-46 Providing multidisciplinary team-based clinical care in the ICU is known to improve outcomes and the Institute for Healthcare Improvement recommends implementation of multidisciplinary team rounds in the ICU.47 Large tertiary facilities, outreach organizations, as well as insurance companies have empowered nurses to improve patient care by enabling
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them to shape patients’ plans of care.4 There has been less overt advocacy around multidisciplinary team leadership, specifically for quality improvement initiatives. The current and future focus of health care is quality and patient safety. Nurses in general, and ICU nurses in specific,48 have often been described as patient advocates. Who better to lead initiatives to ensure high quality and safe care? FINANCIAL DISCLOSURES None Reported. eLetters Now that you’ve read the article, create or contribute to an online discussion on this topic. Visit www.ajcconline.org and click “Submit a response” in either the full-text or PDF view of the article. REFERENCES 1. Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human: Building a Safer Health System. Institute of Medicine. Washington, DC: National Academies Press, 2000. 2. Value-Based Payment Modifier. Centers for Medicare & Medicaid Services website. http://www.cms.gov/Medicare /Medicare-Fee-for-Service-Payment/PhysicianFeedbackProgram/ValueBasedPaymentModifier.html. Accessed October 10, 2014. 3. Arora S, Singh PM, Goudra BG, Sinha AC. Changing trends of hemodynamic monitoring in ICU—from invasive to noninvasive methods: Are we there yet? Int J Crit Illn Inj Sci. 2014;4(2):168-177. 4. The Future of Nursing: Leading Change, Advancing Health. Institute of Medicine. Washington, DC: National Academies Press, 2010. 5. Levy MM, Rhodes A, Phillips GS, et al. Surviving Sepsis Campaign: association between performance metrics and outcomes in a 7.5-year study. Crit Care Med. 2014. 6. Lipitz-Snyderman A, Steinwachs D, Needham DM, Colantuoni E, Morlock LL, Pronovost PJ. Impact of a statewide intensive care unit quality improvement initiative on hospital mortality and length of stay: retrospective comparative analysis. BMJ. 2011;342:d219. 7. Health Care Infection Control Practices Advisory Committee (HICPAC). Centers for Disease Control and Preventon. 2009. Available at: http://www.cdc.gov/hicpac/about.html. Accessed October 7, 2014. 8. Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q. 1966;44(3):Suppl:166-206. 9. Birkmeyer JD, Dimick JB, Birkmeyer NJ. Measuring the quality of surgical care: structure, process, or outcomes? J Am Coll Surg. 2004;198(4):626-632. 10. Wilcox ME, Chong CA, Niven DJ, et al. Do intensivist staffing patterns influence hospital mortality following ICU admission? A systematic review and meta-analyses. Crit Care Med. 2013;41(10):2253-2274. 11. Carson SS, Stocking C, Podsadecki T, et al. Effects of organizational change in the medical intensive care unit of a teaching hospital: a comparison of “open” and “closed” formats. JAMA. 1996;276(4):322-328. 12. West E, Barron DN, Harrison D, Rafferty AM, Rowan K, Sanderson C. Nurse staffing, medical staffing and mortality in intensive care: An observational study. Int J Nurs Stud. 2014;51(5):781-794. 13. Kelly DM, Kutney-Lee A, McHugh MD, Sloane DM, Aiken LH. Impact of critical care nursing on 30-day mortality of mechanically ventilated older adults. Crit Care Med. 2014; 42(5):1089-1095. 14. Selecting process measures for clinical quality measurement. National Quality Measures Clearinghouse website. 2014. http://www.qualitymeasures.ahrq.gov/tutorial /ProcessMeasure.aspx. Accessed October 8, 2014. 15. National Quality Measures Clearinghouse website. http: //www.qualitymeasures.ahrq.gov/index.aspx. Accessed October 8, 2014.
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16. Hospital Compare. Medicare.gov website. http://www .medicare.gov/hospitalcompare/search.html?AspxAutoDetectCookieSupport=1#. Accessed October 11, 2014. 17. Osborne NH, Ko CY, Upchurch GR, Dimick JB. The impact of adjusting for reliability on hospital quality rankings in vascular surgery. J Vasc Surg. 2011;53(1):1-5. 18. Dimick JB, Staiger DO, Birkmeyer JD. Ranking hospitals on surgical mortality: the importance of reliability adjustment. Health Serv Res. 2010;45(6 Pt 1):1614-1629. 19. Gershengorn HB, Kocher R, Factor P. Management strategies to effect change in intensive care units: lessons from the world of business. Part II. Quality-improvement strategies. Ann Am Thorac Soc. 2014;11(3):444-453. 20. Wager KA, Schaffner MJ, Foulois B, Swanson Kazley A, Parker C, Walo H. Comparison of the quality and timeliness of vital signs data using three different data-entry devices. Comput Inform Nurs. 2010;28(4):205-212. 21. Smith LB, Banner L, Lozano D, Olney CM, Friedman B. Connected care: reducing errors through automated vital signs data upload. Comput Inform Nurs. 2009;27(5):318-323. 22. Sessler CN, Gosnell MS, Grap MJ, et al. The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients. Am J Respir Crit Care Med. 2002;166(10):1338-1344. 23. Natarajan K, Stein D, Jain S, Elhadad N. An analysis of clinical queries in an electronic health record search utility. Int J Med Inform. 2010;79(7):515-522. 24. Wood J, Finkelstein J. Comparison of automated and manual vital sign collection at hospital wards. Stud Health Technol Inform. 2013;190:48-50. 25. Meccariello M, Perkins D, Quigley LG, Rock A, Qi J. Vital time savings: evaluating the use of an automated vital signs documentation system on a medical/surgical unit. J Healthc Inf Manag. 2010;24(4):46-51. 26. Jones S, Mullally M, Ingleby S, Buist M, Bailey M, Eddleston JM. Bedside electronic capture of clinical observations and automated clinical alerts to improve compliance with an Early Warning Score protocol. Crit Care Resusc. 2011; 13(2):83-88. 27. Bellomo R, Ackerman M, Bailey M, et al. A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med. 2012;40(8):2349-2361. 28. Majid S, Foo S, Luyt B, et al. Adopting evidence-based practice in clinical decision making: nurses’ perceptions, knowledge, and barriers. J Med Libr Assoc. 2011;99(3):229-236. 29. Cowden S, Johnson LC. A process for consolidation of redundant nursing documentation forms. AMIA Annu Symp Proc. 2003:820. 30. Committee on Patient Safety and Health Information Technology; Institute of Medicine, eds. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: National Academies Press, 2011. 31. Williams CS. SHARE: bridging the interoperability gap between EHRs. J Ark Med Soc. 2013;110(5):84-85. 32. The path to interoperability. Office of the National Coordinator for Health Information Technology website. http: //www.healthit.gov/sites/default/files/onc_interoperabilityfactsheet.pdf. September 2013. Accessed October 14, 2014. 33. Mancuso M. Collaborating our way into interoperability. Health Manag Technol. 2014;35(6):24.
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34. Beaulieu-Volk D. EHRs’ interoperability challenge. HIE expansion aimed at helping providers exchange health information safely, but not all services created equally. Med Econ. 2014;91(6):50-53. 35. Le PN. Strategic interoperability unleashing the full potential of EHRs. Health Manag Technol. 2013;34(10):16. 36. Hendrich A, Chow MP, Skierczynski BA, Lu Z. A 36-hospital time and motion study: how do medical-surgical nurses spend their time? Perm J. 2008;12(3):25-34. 37. Anderson S. Deadly consequences: the hidden impact of America’s nursing shortage. National Foundation for American Policy website. September 2007. http://www.nfap.com/pdf /0709deadlyconsequences.pdf. Accessed October 10, 2014. 38. Critical paths for creating data platforms: patient safety: intravenous infusion pump devices. National Quality Forum website. October 31, 2012. http://www.qualityforum .org/Publications/2012/10/Critical_Paths_for_Creating_Data _Platforms__Patient_Safety__Intravenous_Infusion_Pump _Devices.aspx. Accessed October 10, 2014 39. Wong DH, Gallegos Y, Weinger MB, Clack S, Slagle J, Anderson CT. Changes in intensive care unit nurse task activity after installation of a third-generation intensive care unit information system. Crit Care Med. 2003;31(10):2488-2494. 40. Preparing and Presenting Performance Data: Module 9. Agency for Healthcare Research and Quality website. May 2013. http://www.ahrq.gov/professionals/preventionchronic-care /improve/system/pfhandbook/mod9.html. Accessed October 10, 2014. 41. Keeping Patients Safe: Transforming the Work Environment of Nurses. Institute of Medicine. Washington, DC: National Academies Press; 2004. 42. Marsteller JA, Sexton JB, Hsu YJ, et al. A multicenter, phased, cluster-randomized controlled trial to reduce central line-associated bloodstream infections in intensive care units. Crit Care Med. 2012;40(11):2933-2939. 43. Berenholtz SM, Pronovost PJ, Lipsett PA, et al. Eliminating catheter-related bloodstream infections in the intensive care unit. Crit Care Med. 2004;32(10):2014-2020. 44. Jallo N, Bray K, Padden MP, Levin D. A nurse-driven quality improvement program to improve perinatal outcomes. J Perinat Neonatal Nurs. 23(3):241-250. 45. Friesecke S, Schwabe A, Stecher SS, Abel P. Improvement of enteral nutrition in intensive care unit patients by a nursedriven feeding protocol. Nurs Crit Care. 2014;19(4):204-210. 46. Krimsky WS, Mroz IB, McIlwaine JK, et al. A model for increasing patient safety in the intensive care unit: increasing the implementation rates of proven safety measures. Qual Saf Health Care. 2009;18(1):74-80. 47. Kim MM, Barnato AE, Angus DC, Fleisher LA, Kahn JM. The effect of multidisciplinary care teams on intensive care unit mortality. Arch Intern Med. 2010;170(4):369-376. 48. Breeding J, Turner de S. Registered nurses’ lived experience of advocacy within a critical care unit: a phenomenological study. Aust Crit Care. 2002;15(3):110-117.
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Quality Improvement Strategies for Critical Care Nursing Amanda B. Barnhorst, Mirian Martinez and Hayley B. Gershengorn Am J Crit Care 2015;24:87-92 doi: 10.4037/ajcc2015104 © 2015 American Association of Critical-Care Nurses Published online http://www.ajcconline.org Personal use only. For copyright permission information: http://ajcc.aacnjournals.org/cgi/external_ref?link_type=PERMISSIONDIRECT
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QUALITY IMPROVEMENT STRATEGIES FOR CRITICAL CARE NURSING By Amanda B. Barnhorst, MD, MHA, Mirian Martinez, RN, PCC, and Hayley B. Gershengorn, MD
E
vidence-based practice and performance improvement have become familiar terms in developed health care settings over the past 2 decades. The major instigator for better quality health care was the Institute of Medicine’s (IOM’s) report in 2000, To Err is Human,1 proposing that medical errors are one of the leading causes of death in the United States. As a result, large organizations, both private and governmental, now encourage evidence-based care by setting specific quality and safety initiatives for health care providers to follow. Although cooperation is not always mandatory, failure to collect data and/or comply with these initiatives can result in loss in reimbursement.2 Outside these “mandated” initiatives, providers in individual hospitals and units—including the intensive care unit (ICU)—are also encouraged to identify measures important to their specific situations. Improving care in the ICU should be a goal of every provider in the unit. Critically ill patients undergo extensive invasive testing, may have several monitoring devices, and can be receiving multiple medications. This situation often places them at a greater risk for potentially preventable conditions that can be associated with high morbidity and mortality such as iatrogenic injuries, hospital–acquired infections, pressure ulcers, and delirium.3 The nursing profession currently comprises the largest segment of the nation’s health care workforce4 and generally has the most immediate and most consistent patient contact. Critical care nurses often perform most of the care, patient assessments, and evaluations in the ICU, which places them in the perfect position to © 2015 American Association of Critical-Care Nurses doi: http://dx.doi.org/10.4037/ajcc2015104 www.ajcconline.org
identify, initiate, evaluate, and sustain quality initiatives. Another IOM report, The Future of Nursing: Leading Change, Advancing Health stresses nurses’ fundamental role in transforming our health care system so that it “provides seamless, affordable, quality care that is accessible to all and leads to improved health outcomes.”4 This paper will provide guidance in choosing appropriate measures for quality initiatives in an ICU setting, assuring accurate data collection, providing meaningful results, and implementing change. Specific focus will be placed on how nurses can partake in and lead these initiatives.
Choose the Right Metric One of the first steps in developing a quality initiative is deciding what to measure. The metric must both accurately gauge the quality of care and be reliably measurable. Concrete outcomes such as mortality are common metrics used for quality initiatives but these metrics may not always be appropriate for initiatives in the ICU.5,6 For example, mortality in a patient with a severe stroke progressing to brain death does not necessarily give an accurate measure of quality of care. A more appropriate metric in this case may include use of end-of-life care such as palliative care or organ donation. Metrics should be relevant to the patient, those caring for the patient, and the health care organization. An example of one metric currently measured in the ICU that fulfills the above criteria is catheter-related urinary tract infections (CAUTIs). This metric is tracked and reported because CAUTIs are one of the most common health care–associated infections and are associated with increased morbidity, mortality, hospital cost, and length of stay.7 This measure is relevant to both the patient and the organization because decreases in CAUTI rates may improve
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“
Quality metrics can be divided into 3 broad categories: structural, process, and clinical or health outcomes.
patient outcomes as well as potentially decrease health care costs. First described by Donabedian in 19668 and recently applied to surgical outcomes by Birkmeyer and colleagues,9 quality metrics can be divided into 3 broad categories: structural, process, and clinical or health outcomes. Structural measures reflect the system or setting in which the health care is delivered. Examples of structural measures in a critical care setting include the level of training for ICU staff and physicians, staffing models, and open versus closed ICUs. Although they are not direct measures of care quality, such structural differences can be associated with high- or low-quality care.10-12 For example, studies have shown that patients in hospitals with better critical care nurse work environments and higher proportions of critical care nurses with a bachelor's degree in nursing experienced significantly lower odds of death.13 Structural measures, such as nursing education, are often fairly easy to quantify. But, for some hospitals, the association between a structural measure and high quality care may not be obvious; moreover, financial and staff constraints may render collection of some structural data arduous. The second category, process measures, describes the care that the patient actually receives. These measures evaluate adherence to various protocols and can be linked to outcomes. They are often used to compare one unit or hospital with another and are usually based on best available evidence such as randomized control trials and systematic reviews.14 Initiatives to improve CAUTI rates are examples of initiatives that often employ measurement of process metrics; such initiatives are a focus of quality improvement programs because decreasing CAUTI
About the Authors Amanda B. Barnhorst is chief critical care fellow, Mirian Martinez is a research nurse and quality assurance nurse, and Hayley B. Gershengorn is an assistant professor at the Jay B. Langner Critical Care System, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York. Corresponding author: Amanda B. Barnhorst, MD, MHA, Division of Critical Care Medicine, Montefiore Medical Center, 111 E 210th St, Gold Zone, Main Floor, Bronx, NY 10467. (E-mail: [email protected]).
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rates has a positive impact on outcome.7 The Centers for Disease Control and Prevention’s CAUTI guidelines cite multiple studies that have identified specific interventions that may reduce CAUTI rates.7 These include catheter insertion only for appropriate indications, maintenance of catheters only as long as needed, insertion by properly trained persons using aseptic technique and sterile equipment, and maintenance of unobstructed urine flow.7 There are difficulties with initiatives based on the measurement of process metrics, however. First, what is considered best practice today may not be considered best practice tomorrow due to the evolution of research. Second, having best-practice processes of care does not necessarily mean that the actual care delivered is of high quality (eg, the use of proper urinary catheter technique may not assure low CAUTI rates). The last category is health or clinical outcome measures. The National Quality Measures Clearinghouse, a public resource for evidence-based quality measures and measure sets, defines a health or clinical outcome as “a health state of a patient resulting from health care.”15 Examples of these measures include mortality rates, complication rates, length of stay, readmission rates, patient satisfaction, functional health status, and other measures of health-related quality of life. Using the example of CAUTIs, the comparison of CAUTI rates among units and hospitals is an example of a clinical or health outcome. These rates are often publically reported and can be helpful when patients are choosing a specific health care entity such as a nursing home or surgeon.16 Caution must be used when interpreting these rates, however, because rates calculated for hospitals, units, or physicians with limited numbers of patients can be grossly inaccurate.17,18 Birkmeyer and colleagues9 discuss a paradigm to help choose metrics based on the risk and the frequency of the procedure being studied. They propose that high-risk, low frequency procedures should be evaluated by structure based measures; low-risk, high-frequency procedures should be evaluated using process measures; and high risk, high-frequency procedures should be evaluated by outcome measures. Gershengorn and colleagues19 introduce a flow diagram to help select metrics
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When planning for data collection, it is important to consider who will be capturing the data and how data collection will affect their workload.
”
1. 2. 3. 4.
Is it: Identifiable? Measureable? Relevant? Timely?
yes
Measure metric
Is quality/ performance acceptable?
yes
no no
yes
Process 1. 2. 3. 4.
Choose process metric (eg, % of time right antibiotic is chosen)
Is it: Identifiable? Measureable? Relevant? Timely?
yes
Measure metric
Is quality/ performance acceptable?
no
no no yes
Structure 1. 2. 3. 4.
Choose structure metric (eg, availability of resources to look-up right antibiotics)
Is it: Identifiable? Measureable? Relevant? Timely?
yes
Measure metric
Is quality/ performance acceptable?
no
Initiate programs to improve on chosen metric
Select an event to evaluate (eg, administration of early, appropriate antibiotics)
Choose outcome metric (eg, % of time antibiotic given as desired)
Choose a new project
Outcome
no
Figure Algorithm for appropriate metric identification. Reprinted with permission of the American Thoracic Society. Copyright ©2014 American Thoracic Society. Official Journal of the American Thoracic Society.19
starting with outcome and progressing to process and then structure until a good starting metric is identified (see Figure). A key component to metric selection, however, is interaction with frontline clinicians who know the structures and processes of care best. Including these providers (eg, the bedside nurse, the clinical pharmacist, the physical/occupational therapist, the respiratory therapist) in metric selection at the beginning will not only improve on the data collection but will ensure buy-in during initiative implementation.
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Optimize Data Collection Once the measures are chosen, the next step is to determine how to accurately capture and record the data. Historically, patient data was written on paper and abstraction consisted of reading notes handwritten by health care providers. A chart abstractor would read the charts, decipher the handwriting, and attempt to pull out relevant data to be entered in a separate database. This form of data collection is resource intensive and often fraught with error. Wager and colleagues20 evaluated the accuracy and timeliness of physiologic
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Table Methods for data display Chart Type
Definition
Ideal use
Run charts Control charts
Display observed data in an ordered sequence
Easily display trends, shifts, and stability of data
Pareto charts
Individual values are plotted in descending order on the x axis; line graph plots cumulative frequency
Focuses the reader’s eye on the factors that have the greatest impact and where an intervention is likely to produce the greatest benefit
Waterfall charts Bar chart that displays Helps target both positive both positively and negand negative influences on atively influencing varia product and easily display ables and their impact the magnitude of each on the cumulative effect Scorecards and dashboards
Displays many different indictors together
Can be used to compare individual data with benchmarks. Are often are brightly colored and target areas of improvement.
data entry by technicians and nurses before and after electronic health record (EHR) implementation at a large tertiary hospital. They found that when using paper charting, the error rate approached 17% but when transitioned to data entry using an easily assessable mobile device, error rates fell to < 6%.20 With the introduction of EHRs, data can be captured in real time at the bedside and either automatically entered into a database or manually entered into a computer using an interface.21 Bedside data includes variables such physiologic data points, medication administration records, and assessments such as pain or Richmond Agitation-Sedation Scale.22 If this data is entered and saved using defined formats, it generally can be easily mined and analyzed. For example, when capturing a time of medication administration, if it is entered into a field defined and formatted as a standard unit of time (eg, mm/dd/yyyy or hh:mm), mining, analyzing, and displaying this data can be automated. However, if data is entered into an EHR using free text (daily progress or shift notes); searching for data is more difficult.23 The use of EHRs for data collection may eliminate data transcription errors,24-27 but may introduce new types of data entry inaccuracies. For example, equipment malfunction may result in erroneous data—some patient monitoring systems will automatically record all blood pressures taken even if the cuff is not connected to the patient, and this results in the collection of an inaccurate measurement. Additionally, the use of EHRs does not replace the need to be clear and precise about what data is to be collected. For example, “time zero” for sepsis patients means very different
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things to different individuals. For the emergency department nurse, this is often the time that the patient arrives at triage. For the ICU nurse, this may mean when the patient arrives to the ICU. No matter how the data is collected, without clarity on this issue, all data will be unreliable. Although the implementation of EHRs has been an important step towards consistent and quality health care, EHRs have certainly created additional burdens on providers.28 Many providers complain that the EHR has taken away face time with patients and can often produce redundancy.29 Some institutions maintain mixed electronic and paper medical records due to the costs of implementing the EHR.30 Other systems have not yet maximized interoperability (connections between different software applications that make it possible for unaffiliated providers to directly communicate).31-35 These setups can produce not only redundancy and data errors, but also contempt among those entering duplicate data. When planning for data collection, therefore, it is important to consider who will be capturing the data and how data collection will affect their workload. For example, bedside nurses or technicians are already capturing many data points. In fact, some studies have shown nurses spend nearly 30% of their day documenting while only 7% of their time is spent assessing the patients.36 Studies have also shown an association between better patient outcomes and more nursing time per patient-day.37 Therefore, adding additional documentation time to already overloaded nurses can potentially have a negative impact on patient care. An alternative approach may be to utilize current technology to automate data transfer from patient devices such as patient monitors, smart beds, and infusion pumps.38 A 2003 study indicated that ICU nurses can save up to 30% of documentation time when physiologic data is automated.39 As the nurse is often the provider most aware of all that is happening with and for a patient, he or she may be the best-positioned person to advise on how data collection can be streamlined into usual care activities.
Disseminate Data Once baseline data is collected and analyzed, the next step is dissemination to the team(s) involved. This step is important to keep the team engaged40 and can often be best accomplished by champions from different disciplines who have been involved in the project from its inception. Using charts and visual effects will strategically draw the viewer’s eye to the significant results. Ensure that values are clearly labeled on charts and legends are provided. The Table
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gives examples of various types of charts and their ideal usage. Finally, the method for public presentation of the data must be determined. In some settings, displays of unit-wide results in full view in patient-care areas may be appropriate whereas for others, oneon-one conversations about results with providers affected by the initiative may prove best. Many factors will go into shaping this decision including the unit’s culture, the type of initiative, and the general public’s interest in the results. Although not always practical, more public displays can engender friendly competition among units and increase enthusiasm for initiatives with positive results being a source of pride for participants.
Empower Nurses Because they make up the largest segment of the health care workforce4 and are on the frontline, nurses are well positioned to impact great change. Many critical care nurses only perform clinical duties in a single unit, thus they identify this unit as “home.” These nurses often feel motivated and empowered to improve care when possible. According to the 2004 IOM report, Keeping Patients Safe: Transforming the Work Environment of Nurses, “how well we are cared for by nurses affects our health, and sometimes can be a matter of life or death.”41 The IOM’s 2010 report addressing the future of nursing calls for nurses to be “full partners, with physicians and other health care professionals, in redesigning health care in the United States.”4 When leading quality initiatives, nurses have and will continue to play integral roles. For example, the recent randomized controlled trial evaluating checklists to decrease central–line associated blood stream infections (CLABSIs), was initiated and led by nursing.42 In this study, central venous catheters were placed in the intervention group using an infection prevention bundle previously described by Berenholtz et al.43 The intervention group was found to have a 70% reduction in mean number of CLABSIs by the end of phase 1 compared to 21% in the control group.42 Other studies striving to improve outcomes in preterm birth, enteral nutrition, and ventilator-associated pneumonia rates were all nurse-driven and had significant positive results.44-46 Providing multidisciplinary team-based clinical care in the ICU is known to improve outcomes and the Institute for Healthcare Improvement recommends implementation of multidisciplinary team rounds in the ICU.47 Large tertiary facilities, outreach organizations, as well as insurance companies have empowered nurses to improve patient care by enabling
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them to shape patients’ plans of care.4 There has been less overt advocacy around multidisciplinary team leadership, specifically for quality improvement initiatives. The current and future focus of health care is quality and patient safety. Nurses in general, and ICU nurses in specific,48 have often been described as patient advocates. Who better to lead initiatives to ensure high quality and safe care? FINANCIAL DISCLOSURES None Reported. eLetters Now that you’ve read the article, create or contribute to an online discussion on this topic. Visit www.ajcconline.org and click “Submit a response” in either the full-text or PDF view of the article. REFERENCES 1. Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human: Building a Safer Health System. Institute of Medicine. Washington, DC: National Academies Press, 2000. 2. Value-Based Payment Modifier. Centers for Medicare & Medicaid Services website. http://www.cms.gov/Medicare /Medicare-Fee-for-Service-Payment/PhysicianFeedbackProgram/ValueBasedPaymentModifier.html. Accessed October 10, 2014. 3. Arora S, Singh PM, Goudra BG, Sinha AC. Changing trends of hemodynamic monitoring in ICU—from invasive to noninvasive methods: Are we there yet? Int J Crit Illn Inj Sci. 2014;4(2):168-177. 4. The Future of Nursing: Leading Change, Advancing Health. Institute of Medicine. Washington, DC: National Academies Press, 2010. 5. Levy MM, Rhodes A, Phillips GS, et al. Surviving Sepsis Campaign: association between performance metrics and outcomes in a 7.5-year study. Crit Care Med. 2014. 6. Lipitz-Snyderman A, Steinwachs D, Needham DM, Colantuoni E, Morlock LL, Pronovost PJ. Impact of a statewide intensive care unit quality improvement initiative on hospital mortality and length of stay: retrospective comparative analysis. BMJ. 2011;342:d219. 7. Health Care Infection Control Practices Advisory Committee (HICPAC). Centers for Disease Control and Preventon. 2009. Available at: http://www.cdc.gov/hicpac/about.html. Accessed October 7, 2014. 8. Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q. 1966;44(3):Suppl:166-206. 9. Birkmeyer JD, Dimick JB, Birkmeyer NJ. Measuring the quality of surgical care: structure, process, or outcomes? J Am Coll Surg. 2004;198(4):626-632. 10. Wilcox ME, Chong CA, Niven DJ, et al. Do intensivist staffing patterns influence hospital mortality following ICU admission? A systematic review and meta-analyses. Crit Care Med. 2013;41(10):2253-2274. 11. Carson SS, Stocking C, Podsadecki T, et al. Effects of organizational change in the medical intensive care unit of a teaching hospital: a comparison of “open” and “closed” formats. JAMA. 1996;276(4):322-328. 12. West E, Barron DN, Harrison D, Rafferty AM, Rowan K, Sanderson C. Nurse staffing, medical staffing and mortality in intensive care: An observational study. Int J Nurs Stud. 2014;51(5):781-794. 13. Kelly DM, Kutney-Lee A, McHugh MD, Sloane DM, Aiken LH. Impact of critical care nursing on 30-day mortality of mechanically ventilated older adults. Crit Care Med. 2014; 42(5):1089-1095. 14. Selecting process measures for clinical quality measurement. National Quality Measures Clearinghouse website. 2014. http://www.qualitymeasures.ahrq.gov/tutorial /ProcessMeasure.aspx. Accessed October 8, 2014. 15. National Quality Measures Clearinghouse website. http: //www.qualitymeasures.ahrq.gov/index.aspx. Accessed October 8, 2014.
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16. Hospital Compare. Medicare.gov website. http://www .medicare.gov/hospitalcompare/search.html?AspxAutoDetectCookieSupport=1#. Accessed October 11, 2014. 17. Osborne NH, Ko CY, Upchurch GR, Dimick JB. The impact of adjusting for reliability on hospital quality rankings in vascular surgery. J Vasc Surg. 2011;53(1):1-5. 18. Dimick JB, Staiger DO, Birkmeyer JD. Ranking hospitals on surgical mortality: the importance of reliability adjustment. Health Serv Res. 2010;45(6 Pt 1):1614-1629. 19. Gershengorn HB, Kocher R, Factor P. Management strategies to effect change in intensive care units: lessons from the world of business. Part II. Quality-improvement strategies. Ann Am Thorac Soc. 2014;11(3):444-453. 20. Wager KA, Schaffner MJ, Foulois B, Swanson Kazley A, Parker C, Walo H. Comparison of the quality and timeliness of vital signs data using three different data-entry devices. Comput Inform Nurs. 2010;28(4):205-212. 21. Smith LB, Banner L, Lozano D, Olney CM, Friedman B. Connected care: reducing errors through automated vital signs data upload. Comput Inform Nurs. 2009;27(5):318-323. 22. Sessler CN, Gosnell MS, Grap MJ, et al. The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients. Am J Respir Crit Care Med. 2002;166(10):1338-1344. 23. Natarajan K, Stein D, Jain S, Elhadad N. An analysis of clinical queries in an electronic health record search utility. Int J Med Inform. 2010;79(7):515-522. 24. Wood J, Finkelstein J. Comparison of automated and manual vital sign collection at hospital wards. Stud Health Technol Inform. 2013;190:48-50. 25. Meccariello M, Perkins D, Quigley LG, Rock A, Qi J. Vital time savings: evaluating the use of an automated vital signs documentation system on a medical/surgical unit. J Healthc Inf Manag. 2010;24(4):46-51. 26. Jones S, Mullally M, Ingleby S, Buist M, Bailey M, Eddleston JM. Bedside electronic capture of clinical observations and automated clinical alerts to improve compliance with an Early Warning Score protocol. Crit Care Resusc. 2011; 13(2):83-88. 27. Bellomo R, Ackerman M, Bailey M, et al. A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med. 2012;40(8):2349-2361. 28. Majid S, Foo S, Luyt B, et al. Adopting evidence-based practice in clinical decision making: nurses’ perceptions, knowledge, and barriers. J Med Libr Assoc. 2011;99(3):229-236. 29. Cowden S, Johnson LC. A process for consolidation of redundant nursing documentation forms. AMIA Annu Symp Proc. 2003:820. 30. Committee on Patient Safety and Health Information Technology; Institute of Medicine, eds. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: National Academies Press, 2011. 31. Williams CS. SHARE: bridging the interoperability gap between EHRs. J Ark Med Soc. 2013;110(5):84-85. 32. The path to interoperability. Office of the National Coordinator for Health Information Technology website. http: //www.healthit.gov/sites/default/files/onc_interoperabilityfactsheet.pdf. September 2013. Accessed October 14, 2014. 33. Mancuso M. Collaborating our way into interoperability. Health Manag Technol. 2014;35(6):24.
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34. Beaulieu-Volk D. EHRs’ interoperability challenge. HIE expansion aimed at helping providers exchange health information safely, but not all services created equally. Med Econ. 2014;91(6):50-53. 35. Le PN. Strategic interoperability unleashing the full potential of EHRs. Health Manag Technol. 2013;34(10):16. 36. Hendrich A, Chow MP, Skierczynski BA, Lu Z. A 36-hospital time and motion study: how do medical-surgical nurses spend their time? Perm J. 2008;12(3):25-34. 37. Anderson S. Deadly consequences: the hidden impact of America’s nursing shortage. National Foundation for American Policy website. September 2007. http://www.nfap.com/pdf /0709deadlyconsequences.pdf. Accessed October 10, 2014. 38. Critical paths for creating data platforms: patient safety: intravenous infusion pump devices. National Quality Forum website. October 31, 2012. http://www.qualityforum .org/Publications/2012/10/Critical_Paths_for_Creating_Data _Platforms__Patient_Safety__Intravenous_Infusion_Pump _Devices.aspx. Accessed October 10, 2014 39. Wong DH, Gallegos Y, Weinger MB, Clack S, Slagle J, Anderson CT. Changes in intensive care unit nurse task activity after installation of a third-generation intensive care unit information system. Crit Care Med. 2003;31(10):2488-2494. 40. Preparing and Presenting Performance Data: Module 9. Agency for Healthcare Research and Quality website. May 2013. http://www.ahrq.gov/professionals/preventionchronic-care /improve/system/pfhandbook/mod9.html. Accessed October 10, 2014. 41. Keeping Patients Safe: Transforming the Work Environment of Nurses. Institute of Medicine. Washington, DC: National Academies Press; 2004. 42. Marsteller JA, Sexton JB, Hsu YJ, et al. A multicenter, phased, cluster-randomized controlled trial to reduce central line-associated bloodstream infections in intensive care units. Crit Care Med. 2012;40(11):2933-2939. 43. Berenholtz SM, Pronovost PJ, Lipsett PA, et al. Eliminating catheter-related bloodstream infections in the intensive care unit. Crit Care Med. 2004;32(10):2014-2020. 44. Jallo N, Bray K, Padden MP, Levin D. A nurse-driven quality improvement program to improve perinatal outcomes. J Perinat Neonatal Nurs. 23(3):241-250. 45. Friesecke S, Schwabe A, Stecher SS, Abel P. Improvement of enteral nutrition in intensive care unit patients by a nursedriven feeding protocol. Nurs Crit Care. 2014;19(4):204-210. 46. Krimsky WS, Mroz IB, McIlwaine JK, et al. A model for increasing patient safety in the intensive care unit: increasing the implementation rates of proven safety measures. Qual Saf Health Care. 2009;18(1):74-80. 47. Kim MM, Barnato AE, Angus DC, Fleisher LA, Kahn JM. The effect of multidisciplinary care teams on intensive care unit mortality. Arch Intern Med. 2010;170(4):369-376. 48. Breeding J, Turner de S. Registered nurses’ lived experience of advocacy within a critical care unit: a phenomenological study. Aust Crit Care. 2002;15(3):110-117.
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Quality Improvement Strategies for Critical Care Nursing Amanda B. Barnhorst, Mirian Martinez and Hayley B. Gershengorn Am J Crit Care 2015;24:87-92 doi: 10.4037/ajcc2015104 © 2015 American Association of Critical-Care Nurses Published online http://www.ajcconline.org Personal use only. For copyright permission information: http://ajcc.aacnjournals.org/cgi/external_ref?link_type=PERMISSIONDIRECT
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