A graduate certificate will generally take less time and cost less money than a master’s degree. This makes certificate programs in data science an attractive option if you have limited resources, or want to get a taste for data science before committing to more long-term study.
More specialized certificate programs—those in urban informatics, for example — may appeal to you if you’re already working in data science, or want to add a concentration to a master’s degree you’re currently pursuing.
Why a Certificate?
Post-baccalaureate certificates typically provide a relatively quick way to get up to speed in a specialized or job-specific field of study. They’re common in a number of fields, like business and cyber-security, and it’s no different in data science.
Reasons to pursue a graduate certificate in data science or a related specialty include:
- You want to advance in your current job. Maybe you’re a financial, business, or systems analyst keen to master the latest data analysis methods. Or a computer scientist or statistician itching to dig into big data. Or a manager interested in harnessing data science to improve business performance. A certificate in data science or a related specialty could provide just the tools you need.
- You want to switch careers (or you’re just starting out). In a world awash in data, those who can make sense of it are in demand. Whether you’re fresh out of college with some statistics and computer programming under your belt or mid-career but looking for a change, why not aspire to what the Harvard Business Review called “the sexiest job of the 21st century,” data scientist? Certificate programs provide a flexible, low-risk way for you to explore this career path. In many cases, credits earned for a certificate can be applied toward a master’s degree, should you decide later on to delve into data science more deeply.
- You want to specialize. Some certificate programs are designed to be completed in addition to a master’s program, to give you more experience with and in-depth understanding of a subfield of data science. Some students enroll in such certificate programs while still pursuing their master’s degrees; others wait until afterward.
Graduate certificates in data science may be general (“data science”) or specific (e.g., “urban informatics,” “business analytics”). General certificates are more likely to suit your needs if you’re a recent graduate or someone looking to break into data science from a different field. If, on the other hand, you work or have a degree in data science already, consider the more specialized programs.
Certificate programs vary in the number of courses they require — generally between three and seven — and the extent to which you can tailor the curriculum to fit your interests. A few programs require students to complete a consulting project or other capstone.
Typical courses include:
- Statistical Methods for Data Science
- Data Mining
- Machine Learning
- Information Visualization
- Database Management Systems
Many certificate programs in data science require a bachelor’s degree in computer science, statistics, mathematics, engineering, or a related field, though in some cases relevant work experience will suffice. Familiarity with databases, programming languages, and/or statistics will give you a leg up.
Other Things to Think About
Ready to pursue a certificate in data science? Be sure you don’t ignore:
- Cost. While the sticker price of a certificate may be lower than that of a master’s degree, it’s harder to qualify for financial aid as non-degree student. Students enrolled in graduate certificate programs may qualify for student loans, but only if the university has sought and secured U.S. Department of Education approval for that specific program. So, read the small print carefully and, when in doubt, barrage program contacts with questions.
- Transferability. Ask yourself whether a certificate alone will meet your educational needs. If there’s a possibility you’ll want to pursue a master’s degree eventually, make sure you choose a certificate program in which the courses you take can be applied toward that more advanced degree.
We found 129 universities in our directory offering graduate certificate programs in data science and related specialties. If you represent a university and would like to contact us about editing any of our listings, or adding new programs, please send an email to info (at) mastersindatascience.org.
Certificate in Data Science/Analytics Online
Click here to see the full list of on-campus programs instead.
California State University-Fullerton
Certificate in Data Science
California State University Fullerton offers an online Certificate in Data Science through its Professional Development, University Extended Education program. The certificate program includes five courses and consists of 99 hours of lecture and discussions, resulting in 9.9 Continuing Education Units for students who earn the certificate. Applicants should have a bachelor's degree in computer science, applied statistics, engineering, math, physics, or natural sciences. Alternately, applicants can show they have at least three years of professional experience in a field such as data analysis, business analytics or intelligence, software engineering or programming. Courses are offered online in the spring, summer and fall. The program's advisory board includes representatives from IBM, Wells Fargo, and Gravity.com as well as from CSUF.
Central Connecticut State University
Graduate Certificate in Data Mining
Central Connecticut State University offers a five-course, 18-credit program leading to a Graduate Certificate in Data Mining that can be completed entirely online, with no requirement to go to campus. Applicants to the program must have a bachelor's degree with a GPA of at least 3.0. Additionally, applicants must have passed at least two semester courses in statistics at the undergraduate or graduate level. Students who start the graduate certificate program may transfer the credits to the Master of Science in Data Mining, but they must submit a separate application for that degree program. The CCSU faculty has broad experience in data mining in fields such as text mining, web mining and genomics. Faculty members have written five books on the subject of data mining.
Colorado State University-Global Campus
Certificate in Business Intelligence
Colorado State University's Global Campus offers an online Graduate Certificate in Business Intelligence with no requirements to attend any face-to-face sessions. To earn the certificate, students must complete four graduate-level courses totaling 12 credits. In the program, students learn how to collect and analyze statistical information, spot trends, make projections, and use data to drive strategic plans. Students have the option of earning a stand-alone graduate certificate to increase their skill sets and earning potential, or of using the specialization to customize a master's degree. The business intelligence specialization is available with nine Global Campus master's programs, including those leading to a master's in management, criminal justice, finance, information technology management, and health care administration.
Hawaii Pacific University
Professional Certificate in Business Analytics
Hawaii Pacific University offers a Professional Certificate in Business Analytics designed for working professionals who want to learn more about how information systems and business analytics can make their company more competitive. Classes for the program are available in an online format. To earn the certificate, students must complete four classes covering topics such as big data, business analytics, and data mining for business intelligence. Applicants to the certificate program must have an undergraduate degree with a GPA of at least 2.7. Students must meet any prerequisites set for any of the classes included in the curriculum for the certificate.
Highlands College of Montana Tech
Graduate Certificate in Health Care Informatics
Montana Tech has a 15-credit Graduate Certificate in Health Care Informatics that students can complete entirely online. The curriculum requires students to complete five courses, including four required classes and an approved elective. The program is designed for health care or IT professionals who have a bachelor's degree in a relevant field or a bachelor's degree in any field and relevant work experience. Undergrad GPA must be 3.0 or higher for unconditional admission. Applicants must submit transcripts from all colleges attended, GRE scores, three references, a personal statement, and a resume. Online courses for this program are each offered once a year, in either the spring or fall semester.
Humboldt State University
Geospatial Certificate Program
Humboldt State University offers an online Geospatial Certificate Program that students can complete 100 percent online. The certificate is designed for students who want to enter the field of GIS or who are already working in the field but need coursework to advance their career or earn a license. Applicants must have at least a high school diploma or equivalent, but they also should have the college-level math and writing skills necessary to pass college coursework. Classes are delivered in an accelerated eight-week format, and content includes video lectures, assignments, and participation in online forums. Students can complete the five-course program in 11 months. Cohorts of students are admitted into the program in the fall.
Indiana University Bloomington
Online Certificate in Data Science (Graduate)
Indiana University Bloomington offers an online Certificate in Data Science that provides students a flexible option to tailor coursework to meet their interests or career goals. Completing the certificate requires students to take four courses, which can be selected from a broad range of approved courses offered in topics such as data analysis, health and medicine, cloud computing and high-performance computing. IU's data science program offers a technical path and a path for decision makers. Applicants must include three letters of recommendation, although those with a technical background only need to submit one recommendation. Applicants must also submit college transcripts, a resume and a statement of purpose.
Indiana University-Purdue University-Indianapolis
Health Information Management and Exchange Certificate
The School of Informatics and Computing at Indiana University-Purdue University Indianapolis has an online Health Information Management and Exchange Certificate designed for students who already have an undergraduate degree in some field of health care. The program prepares students to collect, manage, exchange, and analyze electronic data. Students must complete fives courses, all offered completely online. Students also complete a mentored practicum in which they demonstrate their skills in a real-world environment. Applicants must have a bachelor's degree with an overall GPA of at least 3.0. Successful applicants might have a background such as nurse or nurse practitioner, pharmacist, health care administrator, or health researcher. Students may enter the program in the spring or fall semester.
Kennesaw State University
Online Certificate in Applied Statistics using R
Working professionals who want additional training in applied statistics can complete an Online Certificate in Applied Statistics using R through Kennesaw State University. This program is self-paced and requires students to complete five courses. For each course, students will receive video and audio recordings, printed material, data for practice, and self-assessment tests. After completing the work for each course, they must take a proctored exam, either at the Kennesaw State campus or through a proctor in their home area. A cumulative exam is required at the end. The program is open to working professionals who have some background in basic statistics. Applicants should have completed at least one course of college algebra or a higher math. The courses in this program do not carry college credit.
Online Certificate in Applied Statistics using SAS
Kennesaw State University offers an Online Certificate in Applied Statistics using SAS designed for working professionals who need more training in applied statistics. The program is self-paced and requires students to complete five courses. Course materials include audio and video recordings, printed materials, data that can be used for practice, and self-assessment tests. After completing each course, students must take a proctored exam, either at Kennesaw State or with a local proctor. There is also a cumulative test at the end. Students have remote access to Base SAS while in the program. Applicants should have a basic understanding of statistics and data analysis and some background using SAS. No courses in this program can be applied to the M.S. in Applied Statistics at Kennesaw State.
Kent State University at Kent
Graduate Certificate in Health Informatics
Students who want to enter the field of health informatics can earn a Graduate Certificate in Health Informatics from Kent State University. All six courses required for this program are offered in a 100-percent online environment. Courses run for seven weeks, and there are two seven-week terms each semester. Applicants must have a bachelor's degree from an accredited college with a preferred GPA of at least 3.0. However, Kent State does not require any specific undergrad major. Applicants must submit official transcripts, three recommendations, a resume, and a statement of purpose. Up to 12 credits earned in the certificate program can be transferred to the Kent State Master of Science in Health Informatics.
Michigan State University
Master Certificate in Business Analytics
Michigan State University has a three-course program leading to a Master Certificate in Business Analytics. The program is designed for business professionals who want to learn how to interpret and use Big Data and for analytics professionals who want to expand their knowledge base. Each of the three online courses lasts for eight weeks, and each course builds upon the information presented in the previous course. Offered through the Eli Broad College of Business, the courses are delivered in an asynchronous format so students can work on their own schedule. These courses do not carry academic credit, but each is worth 3.5 continuing education credits, or CEUs.
Missouri University of Science and Technology
Certificate in Big Data Management and Analytics
Missouri S&T has a Graduate Certificate in Big Data Management and Analytics that is designed for computing professionals. The curriculum requires students to complete four courses, with two core courses and two restricted electives. Some available courses may have prerequisites the student must satisfy to take the class. Applicants should have a bachelor's degree in computer science, engineering, or another technical field and at least two years of professional experience. Individuals who are already enrolled in a graduate program at Missouri S&T can also enroll in this certificate program. Students who complete the certificate and choose to enter the Master of Science in Computer Science program can apply some of the courses from the certificate toward their degree.
Certificate in Big Data Management and Security
Online students can earn a Graduate Certificate in Big Data Management and Security through Missouri S&T. The program is designed for professionals in the computer field and requires students to complete four courses selected from a menu of approved courses. Applicants must have a bachelor's degree in a technical field such as computer science or engineering and at least two years of related experience. Some available courses may have prerequisites a student must satisfy before enrolling in the course. Most courses are offered in an asynchronous format, although instructors may occasionally require students to be online at a set time. There are never any requirements to come to campus. Some credits from the certificate may be applied to the computer science master's program.
Graduate Certificate in Business Analytics and Data Science
Missouri S&T offers an online program leading to a Graduate Certificate in Business Analytics and Data Science that requires students to complete four courses. There are two required courses, one offered each spring and one each fall. Students select two more courses from a list of restricted electives. The program is open to students who have a bachelor's degree or higher in areas such as technology, engineering, science, the social sciences or business, although they must have the prerequisite knowledge required for each class. Students who successfully complete all four classes with a B average or higher are eligible to enter the MBA program or the Master of Science in Information Science and Technology program, with the certificate courses counting towards the degree.
Graduate Certificate in Business Analytics
Auburn University has a Graduate Certificate in Business Analytics that is designed for students who want to gain basic skills in handling big data for a business. The coursework is available online, and students may enter the program at the start of any semester. The curriculum requires students to complete four specific courses, each worth three credits. Applicants must have a bachelor's degree from an accredited college or university, but no specific major is required. Having a background in statistics is not required, but students will find a foundation in statistics to be helpful. Related work experience can also be helpful to students, but it is not a requirement.
Healthcare Data Analyst Certificate
Bellevue College's Department of Continuing Education offers a Healthcare Data Analyst Certificate that requires 227 hours of coursework. The seven required courses are led by instructors and delivered online. Students can choose a nine-month or 12-month schedule to complete the certificate. Applicants should have a bachelor's degree or an associate degree plus two years of database experience. Before beginning the program, students must have a background in SQL, database design, and Excel. A college-level course in algebra is also a prerequisite. The college recommends students complete coursework for the Certified Associate in Health Information & Management Systems before entering this program. The certificate coursework provides students with hands-on experience using analytical tools for data analysis and visualization and uses case studies and scenarios.
Applied Business Analytics Graduate Certificate
Students enrolled in Boston University's online program leading to an Applied Business Analytics Graduate Certificate can complete the program in nine months. The program includes four courses, each awarded four credits. Before taking one of the required courses, students must take a pre-analytics laboratory, a non-credit, hands-on laboratory that is offered in an online format over a seven-week period. Students are also required to pass a prerequisite course in mathematics for management or pass a waiver test, and online students must complete an online math tutorial. Applicants must have a bachelor's degree and must submit a graduate application. Students must be admitted into the program to receive credit toward the certificate.
Data Analytics Graduate Certificate
Boston University's Metropolitan College has introduced a new online Data Analytics Graduate Certificate that students can complete in eight to 12 months. The program consists of four required courses for a total of 16 credits. Applicants must have a bachelor's degree in a technical field and must take a prerequisite course in quantitative methods for information systems. Students also might have to take prerequisite courses before enrolling in some of the courses required in the curriculum. Applicants have the opportunity to take a waiver test through the Department of Computer Science to prove proficiency in a subject if they do not have an academic record for certain classes.
Graduate Certificate in Business Intelligence
Capella University has an online Graduate Certificate in Business Intelligence that students can complete in as few as eight months. The program includes five courses, all from the MBA program, and students may transfer those credits to the MBA program should they decide to pursue that degree. Capella offers the program in two formats, a traditional structured format and the FlexPath program which allows students to work at their own pace. Applicants to the traditional program must have a bachelor's degree with a GPA of at least 2.3. FlexPath applicants must have a bachelor's degree in business or a similar field with a GPA of 2.5. Alternately, FlexPath applicants with a degree in another field can show five years of relevant professional business experience.
Graduate Certificate in Business Analytics
Dakota State University has an online Business Analytics Graduate Certificate designed for students who are already in the field of IT or business analytics and who want to gain additional skills in dealing with large amounts of data. The certificate is a 12-credit program that requires students to complete four courses. All the courses needed to complete the program are scheduled to be delivered at least one semester each year, so students may be able to complete the certificate in as few as three semesters. Applicants should have a bachelor's degree from an accredited college or university and should have a background in a quantitative field, such as math, computer science, statistics, or operations research.
Certificate in Healthcare Informatics
Drexel University's College of Computing and Informatics offers an online Certificate in Health Care Informatics that is designed for information professionals, clinical personnel and health care support staff. Students who complete the program will have an understanding of health information technology and will be better able to serve as a liaison to between health care professionals and information professionals. Applicants to the program must have a bachelor's degree and must submit a statement of purpose explaining how the certificate will help them to achieve their professional goals. The curriculum includes three graduate-level classes that are delivered fully online.
Graduate Certificate in Data Science
Elmhurst College offers a Graduate Certificate in Data Science that students can complete entirely online. The curriculum includes five courses from the college's Master of Science in Data Science program, with four required courses and one elective. Students can complete the program in one year. Individuals who wish to earn the certificate may enroll as non-degree students. Students who earn the certificate may use the credits toward the M.S. in Data Science and complete the degree by taking five additional courses. However, students who want to earn the master's must meet the admission requirements for that degree, including prerequisite courses in statistics and computer science and at least a year of professional experience.
Graduate Certificate in Business Analytics
Iowa State University has a Graduate Certificate in Business Analytics that students can complete entirely online. The 12-credit program requires students to complete one core course and three approved electives. Applicants must have a bachelor's degree in business or a STEM-related field with a GPA of at least 2.0 in their last 60 credit hours of study. All applicants must have passed a college-level statistics course with a grade of 3.0 or higher within the past five years. Additionally, all applicants should have at least two years of work experience since graduating college. The program is aimed at applicants in jobs such as business analyst, data science, or analytic system designer.
Online Post-Bachelor's Certificate in Government Analytics
Johns Hopkins University offers a Graduate Certificate in Government Analytics that is appropriate for students seeking a stand-alone credential in government analytics and for applicants who want to supplement a related master's degree, such as one in government, public management, or global security studies. The degree can be earned entirely online with classes delivered in a convenient, asynchronous format. The curriculum includes five courses and allows students to develop a specialty area such as advanced statistics, geospatial analysis, public policy analysis, or political behavior. Applicants are not required to have a background in quantitative methods. Admission is open to students from diverse academic backgrounds. Students who take two classes per term can complete the certificate in about three semesters.
Online Post-Master's Certificate in Quantitative Methods
Johns Hopkins University offers a Post Master's Certificate in Quantitative Methods in Applied Economics that allows professionals with an advanced degree to update or expand their knowledge. Students can complete the four-course program in an entirely online format. Applicants should have a master's degree in economics or statistics, and they otherwise must comply with the admission requirements for the Master of Science in Applied Economics, which include submitting recommendations, a resume, official transcripts of all college work, and a statement of purpose. Students may start the program in the fall, spring or summer semester. To earn the certificate, students must successfully complete four courses selected from a list of nine acceptable courses.
Applied Statistics Certificate
Students and professionals who need to develop a background in statistics can earn an online Graduate Certificate in Applied Statistics through Kansas State University. The curriculum requires students to complete at least five graduate-level courses, which can be selected from eight courses offered by the Department of Statistics. Students are allowed to apply up to three credits in coursework taken in another department or at another university toward the certificate. Applicants must have a bachelor's degree with a GPA of 3.0 or higher earned in upper-division coursework. Students can enter the program in the fall, spring, or summer semester.
Graduate Certificate in Data Analytics
Kansas State University has an online Data Analytics Grauduate Certificate with a Data Science track and an Applied Analytics track. Courses are delivered completely online with no need to visit campus. The 15-credit certificate requires students to take at least one course from each track so they acquire knowledge in both disciplines. The required capstone is a team project in data analytics. Applicants should have a bachelor's degree with a 3.0 GPA in their last two years of study. Prerequisites include a course in calculus, a course in statistics, ability to work with computer applications, and programming skills for the data science track. Students may enter the program in the fall, spring, or summer semester.
Advanced Certificate in Business Analytics
Marist College offers an online Advanced Certificate in Business Analytics that is designed to provide students with a background in data analysis and business intelligence. Students also gain hands-on experience with current software and analytics tools. The program is intended for professionals in fields such as marketing and advertising, health care administration, finance, business strategy, or research. A technology or computer science background is not expected. Applicants must have an undergraduate degree and should have passed an introductory statistics course, at the minimum. GRE scores are not required. Admission decisions are based on an applicant's undergraduate academic performance, work experience, recommendations, and personal essay. Students are admitted to the program in the spring or fall semester.
Health Care Informatics Graduate Certificate
Misericordia University offers a Graduate Certificate in Health Informatics that is designed for mid-career health professionals who want to gain skills in health care information. The program is delivered online and is aimed at applicants who already work in the informatics area of health care. The program requires students to complete 18 credits, with five required three-credit courses and an additional course that students select from two options: project management or health care systems analysis and design. The program can be completed in as little as one year. Students who complete the program may be eligible to use the credits towards Misericordia's master's program in health informatics
For professionals, providing quality service and striving for excellence are ethical responsibilities. In many hospitals in the U.S., however, there is evidence indicating current quality improvement (QI) involving nurses is not always driven by their professional accountability and professional values. QI has become more an administrative mandate than an ethical standard for nurses. In this paper, the tension between QI as nurses’ professional ethics and an administrative mandate will be described, and the implicit ideal-reality gap of QI will be examined. The threat to professional nursing posed by the current approach to QI will be examined, and ways to incorporate nursing professional values in a practical QI effort will be explored.
Keywords: nursing care, quality improvement, quality of healthcare, professional ethics
With growing concern about hospital care quality and attention to the need for improvement of care, quality improvement (QI) has become an administrative mandate in U.S. hospitals. Although it is encouraging to see the shift of attention from cost containment alone to improvement of quality, the current approach to QI has the potential to undermine the professional values of nursing. In this paper, an implicit gap between QI as ideal nurses’ professional accountability and the reality of current QI activities will be described. Further, this paper will examine potential threats the current QI approach poses to professional nursing, and explore possibilities for integrating nursing professional values in QI efforts.
Current Approaches to Quality Improvement
There are three interrelated but slightly different views about the cause of the healthcare quality problem: 1) Inefficient healthcare system, 2) lack of systematic quality evaluation, and 3) insufficient staffing. Each perceived cause leads to a different approach to improve quality of care (see table 1).
Causes of Quality Problems and Approaches to Quality Improvement
Inefficient System as a Cause of Quality Problem
From the late 1990s, the quality of healthcare has been a growing concern for many Americans and various reports made it clear that Americans were not receiving the quality care they should be receiving (Chassin, Galvin, & The National Roundtable on Health Care Quality, 1998; Institute of Medicine, 2000, 2001; President's Advisory Commission, 1998). The Institute of Medicine (IOM) concluded that an underlying reason for inadequate quality of care was the outmoded and increasingly complex system in which healthcare was delivered (IOM, 2001).
To solve system-wide problems causing quality deterioration, the concept of quality improvement (QI) was introduced to healthcare from the manufacturing industry (Lighter, 1999). The QI model adopted in healthcare is based on principles that increase productivity, reduce costs, and make institutions more competitive (Bennett & Slavin, 2002).The model focuses on identifying defects and wastes in the hospital’s service line and streamlining the process to produce better outcomes. The goal of QI in healthcare has focused on improving outcomes such as morbidity, medication errors, re-admission, length of stay, and mortality through streamlining treatment process.
Using the principles of the QI model from manufacturing industry, healthcare QI activities became highly centralized and tightly controlled by standards and regulations. Quality of care is perceived as a property of the system rather than a property of individual care providers. This QI model tends to disregard healthcare workers’ expertise and professional judgment, replacing these instead with rules and protocols intended to streamline the complex system.
Lack of Systematic Quality Evaluation
Early on, lack of systematic evaluation tools was identified as a barrier to make measurable improvement on quality. Thus among the IOM QI recommendations (2001), issue of quality evaluation was addressed first, and evaluation tools were rapidly implemented. The evaluation of quality of care is aligned with payment policies and provides strong financial incentives for hospitals. The Joint Commission on Accreditation of Healthcare Organizations started including core quality measures in their accreditation. Hospital associations and health plans such as the Centers for Medicare and Medicaid Services (CMS) began asking hospitals to submit reports about hospital quality measures, and the data have been displayed on the public Hospital Quality Initiative (HQI) Web site (CMS, 2008). Financial incentives are provided for submitting quality evaluation data. Further the public nature of the HQI information pressures hospitals not only to participate in quality evaluation and disclose the data, but also to perform well to remain competitive in the industry (Draper, Felland, Liebhaber, & Melichar, 2008).
To evaluate quality of nursing care separate from overall hospital care, American Nurses Association (ANA) has developed nursing-sensitive indicators including nurse staffing information and patient care outcomes such as pressure ulcers, patient falls, and nosocomial infections (ANA, 1999). This information is currently collected and housed in the National Database of Nursing Quality Indicators® (NDNQI®: 2006). Unlike the HQI, NDNQI® provides hospital unit level national comparative data only to participating hospitals for their internal use in QI activities. Yet, participation to NDNQI® is often driven by administrative interests such as Magnet application, meeting the Joint Commission standards, or nurse retention/recruitment rather than internal motivation to improve quality of care by front-line nurses.
Data collected in the HQI, NDNQI®, and other quality measurements are thoughtful and empirically supported indicators of quality care that make comparison across institutions possible. They also provide benchmarks to hospital and nursing administrators to mark their improvement. However, because they are the measures requested by external QI entities and hospital or nursing administrators, not front-line workers, efforts to collect and use the data to evaluate the quality of care has come to be viewed as an administrative mandatory activity.
Insufficient Staffing as a Cause of Quality Deterioration
Among nurses, quality of care is thought to be related to an inadequate patient-to-nurse ratio. From their perspectives, the primary cause of poor quality care is the substantial increase in nurses’ workloads that came about with shortened lengths of patients’ hospital stays, increased acuity of hospitalized patients, and insufficient nurse staffing as a result of cost containment and the nursing shortage (ANA, 1999; Erlen, 2004; Gordon, 2005; Ludwick & Silva, 2003; Shindul-Rothchild, et al., 1996). Nurses have been exhausted and dissatisfied, and perceive that they have less time to provide adequate nursing care, and patient safety has been compromised (Aiken, Clarke, Sloane, Sochalski, & Silber, 2002; Ludwick & Silva, 2003; Shindul-Rothchild, Long-Middleton, & Berry, 1997; Vahey, Aiken, Sloane, Clarke, & Vargas, 2004). A number of researchers have found relationships between nurse staffing and patients’ outcomes (Aiken, Clarke, Sloane, Lake, & Cheney, 2008; Aiken, et al., 2002; Needleman et al., 2011; Sochalski, 2004; Vahey, et al., 2004). Aiken et al. (2002), for example, found that a higher patient-to-nurse ratio was associated with not only negative patient outcomes, but increased odds of nurses’ burnout and job dissatisfaction, which leave nurses with feelings of disempowerment and moral distress. The resulting decline in morale leads to further deterioration of the quality of care they provide (Erlen, 2004). From nurses’ perspective, insufficient staffing in today’s healthcare system is a major cause of deterioration in hospital care quality.
Therefore, many quality improvement efforts in nursing have focused on decreasing the patient-to-nurse ratio. ANA worked to educate nurses, consumers, and policy makers about nursing contributions to quality care and the importance of keeping sufficient nurses at the bedside for safe and quality health care (ANA, 1999). Nursing-sensitive indicators, now part of NDNQI®, are developed as a tool to generate national data on the relationships between nurse staffing and patient outcomes.
Threats of Current QI Approaches to Professional Nursing
Clearly, significant quality problems exist in the U.S. healthcare system. Both healthcare institutions and the nursing profession have been rigorously trying to change the system to improve the quality of care. These efforts are needed and many nurses welcome the idea of QI. However, by following the rapid movement in recent QI activities without reflecting on the assumptions and the meaning of the activities for nursing professional values and practice, nurses may jeopardize their nursing values, and this can lead to, ultimately, a de-professionalization of nursing. Three particular pitfalls are identified as potential threats to the nursing profession in the QI approaches described above: the focus on quantity of nurses, safety as a quality standard, and QI as a mandatory activity.
The Focus on Quantity of Nurses
A number of studies have provided evidence that a higher patient-to-nurse ratio is associated with a higher patient mortality rate and other negative quality outcomes (Aiken, et al., 2008; Aiken, et al., 2002; Needleman et al., 2011; Sochalski, 2004; Vahey, et al., 2004). Yet, the argument that an increased number of nurses will improve quality of care needs careful consideration. Having sufficient number of nurses in a unit is critical to secure the safety and quality of care. But we also have to ask whether it is only the number of nurses that matters. The question is whether quality, competence, and expertise of the nurses should matter too. Although several researchers have found that nurses with more education, more experience, full-time commitment, and better communication skills help to decrease medical error, patient fall rate, and mortality (Aiken, Clarke, Cheung, Sloane, & Silber, 2003; Blegen, Vaughn, & Goode, 2001; Estabrooks, Midodzi, Cummings, Ricker, & Giovannetti, 2005), the quality of nurses is rarely discussed in current QI efforts. By simply equating quality of care with the quantity of nurses, we may represent nurses as laborers for whom only the headcount matters, not professionals with expertise and specialty knowledge.
This may be a pitfall of the manufacturing QI model as well. As Jennings noted (2003), QI following a manufacturing model does not count on healthcare workers’ expertise and professional judgment to provide quality care. Instead, it recommends using rules and protocols to navigate the complex system and make clinical judgment to achieve agreed upon quality care. Does it mean that quality care can be achieved if the nurses know how to follow protocols rather than use their own professional knowledge and judgment in their practice?
IOM’s approach for QI using the manufacturing model, of course, is based on the assumption that all healthcare workers meet their own professional standards. However, in a 2003 report, the IOM concluded that healthcare professionals were not being adequately prepared to provide the highest quality care possible (IOM, 2003). It discussed the need to return to professional core values and reinforce the professional standards to provide high quality and safe care (O'Rourke, 2006). Unfortunately, in nursing, there has been little consideration to define quality nursing care and set professional standards for quality care (Izumi, Baggs, & Knafl, 2010). Focusing on quantity of nurses without specifying professional standards for quality nursing care would jeopardize the professional status of nursing and turn nursing practice into labor where quality of workers does not matter.
Safety as a Quality Indicator
Nurses need to give careful consideration to what quality nursing care is and how to measure it. Current quality indicators for nursing (ANA, 1999) focus on a narrow aspect of quality: safety. The IOM identified safety as one of six core dimensions of quality (safety, effectiveness, patient-centeredness, timeliness, equity, and efficiency) (Cronenwett et al., 2007; IOM, 2001), and quality measure such as the HQI Web site by CMS include evaluation of safety outcomes as well as effectiveness of care process and patient-centered care. However, in nursing, quality is measured primarily in the form of safety outcomes. Safety is important, yet only one dimension of multifaceted quality care. Evaluation of quality of nursing care should include other dimensions of quality while reflecting on the process through which care is provided and the nursing values underlying the practice.
Nurses experience the threat to patient safety first hand. They are concerned that the current hospital environment often does not allow them to provide safe care. Because safety is a minimum quality need to be assured, it is understandable that the first quality benchmark nurses want to address is patient safety. However, nursing leaders need to keep in mind that safety indicators are a minimum standard for nursing practice; they are indicators for quality assurance, not quality improvement. That is, these indicators neither tell nurses how to improve their care nor inspire them to improve that care. Nurses need to keep patients safe, but also have an ethical responsibility to provide good care beyond minimum requirements and strive for the higher goal of excellence (ANA, 2008; Baily, Bottrell, Lynn, Jennings, & Hastings, 2006; Jameton, 1984). By limiting quality indicators to safety outcomes, there is a risk of setting up nurses to work towards a minimum standard and disregard other aspects of quality nursing care that would appreciate nursing’s professional values and inspire nurses to pursue excellence in their practice.
QI as a Mandatory Activity
Including nurses in QI activities merely as collectors of mandatory data and not inviting their ideas to improve quality also carries a threat to the profession. There are increasing internal and external demands for hospitals to participate in a wide range of QI activities (Draper, et al., 2008). The decisions about which QI activities to carry out are made by administrators. Because nurses are integral to a hospitalized patient’s care and part of the hospital system, nurses are often asked by hospital administrators to collect and document (often duplicative) data for various QI activities (Cassil, 2008). When QI activities are top-down mandatory orders, not internally driven by nurses’ professional ownership and accountability for their practice, the QI activity may become an employee obligation rather than a professional responsibility and nurses may lose interest in improving the quality of their care. The individual nurses may be converted from an educated, professional, and well-situated change agent striving to excellence into a laborer who completes assigned tasks and provides prescribed care that is sufficient to meet minimum standards. Nurses and the profession itself may lose sight of their professional responsibilities to improve the quality of their care if they passively follow mandatory QI activities that do not improve nursing care or allow nurses to use their professional knowledge to make autonomous decisions.
Aligning QI with Nursing Professional Values
Providing quality care that meets a high standard is an ethical responsibility of healthcare professionals. Nurses have a longstanding commitment to improve the quality of care they provide (Lang et al., 2004); and long before healthcare quality became a national concern nurses recognized and expressed concerns about inappropriate healthcare systems jeopardizing the quality of care and their patients’ lives (Gordon, 1997). Therefore, as individuals and as professionals, nurses have welcomed the increased interest in healthcare quality across the nation and disciplines. It gives them hope that finally their concerns will be heard, their patients will be safe, and they will be able to provide care they can be proud of. QI could be a force to redesign the American healthcare system for the benefit of all stakeholders including patients and their families, nurses, physicians, other clinicians, healthcare administrators, and payers. This is an opportunity for the nursing profession to take the lead in redesigning a failing healthcare system using nursing expertise in patient care. Yet, QI could be a double-edged sword for nurses. Strong forces for rapid change can put nurses’ professional values in jeopardy if we do not carefully consider the meanings, assumptions, and impact of QI activities for nursing.
To make the current force for quality improvement align with nursing professional values and for nurses to participate in QI as meaningful members of the team of healthcare providers, we first need to understand what constitutes quality nursing care. In spite of the high level of interest in the quality of nursing care, there is no broadly accepted definition of quality nursing care (Izumi, et al., 2010). Although quality has been thought to be socially constructed, with different meanings for different people and professional groups (Gunther & Alligood, 2002; Koch, 1992), nursing often borrows the definition of quality of healthcare in medicine and has not developed its own definition of quality nursing care. Without a clear definition of quality nursing care, nursing is incapable of explaining what constitute quality nursing care. Therefore, we do not know what competencies and professional standards specific to quality to look for and how to measure and evaluate the quality of nursing care. Nurses need to revisit what values make nursing professional and clarify the standards for quality nursing care accordingly. It will help to avoid two pitfalls addressed above. By setting a standard and clarifying attributes expected for professional nurses, we will be able to count not only the quantity but quality of nurses contribute to quality of care. Also it will provide a conceptual framework to develop tools to evaluate quality of nursing care in addition to safety measure.
Second, developing a system where front-line nurses’ concerns and ideas about QI are heard and reflected in the QI strategies is crucial. Nurses on the front line often have first-hand knowledge about what is working and what is not. They also know how their clinical settings work. Transforming Care at the Bedside (TCAB) is an example of using nurses’ practical knowledge and skills to improve quality of care at point-of-service. TCAB is an initiative led by the Robert Wood Johnson Foundation and the Institute for Healthcare Improvement, and focuses on improving the delivery of care in medical/surgical units by encouraging front-line workers, such as nurses, to take leadership to make system changes in their particular settings (Rutherford, Lee, & Greiner, 2004). Recognizing and recruiting front-line nurses’ knowledge and skills as resources for QI empowers nurses and inspires them to strive toward excellence beyond minimum safety. Empowered nurses can promote changes in culture and the structure of their unit to conduct QI activities tailored to the point-of-service. Nurses educated as a professional champion their ethical responsibility to provide safe and quality care. To date, implementation of TCAB in hospitals is still limited, but TCAB can be a model to promote a system where nurses’ knowledge and skills can be used to make QI activities meaningful to the particular settings.
The Magnet Recognition Program® is another example of a system that aligns nurses’ professional values and expertise with quality improvement in healthcare (Aiken, Havens, & Sloane, 2000; ANA, 2009). One of the characteristics of the Magnet hospitals is administrative support to promote quality nursing care. Without administrative support, implementation of nurse-initiated QI such as TCAB is difficult. In addition, regardless of whether institutions receive the Magnet recognition or not, it is critical to create a system to reflect inputs from front-line nurses into hospital administration because they are the eyes and hands of quality improvement. This can allow hospital administration to make effective changes to meet external quality benchmarks, and also to make necessary changes to solve and improve quality problems internal to particular settings. If the hospital administrators are serious about improving quality of their care, they need to invest in bringing nursing expertise into their administrative strategies.
The third and last recommendation is intertwined with the others. For nurses to improve the quality of care they provide and take an active role in QI activities, they need to be not only clinically competent, but also capable of working in a team as a change agent to improve quality of care. Are we preparing nurses to fulfill this expectation? Nurses need to be creative and effective to provide quality care even when there are fewer than the ideal number of nurses on a unit (Benner, Sutphen, Leonard, & Day, 2010).They need to be effective to address problems and make changes in their unit with limited resources (IOM, 2011). To educate nurses who can meet these expectations, it is vital to re-examine the content and process of nursing education. The Quality and Safety Education for Nurses (QSEN) project provides a resource for nursing faculty to incorporate the knowledge, skills, and attitudes necessary to continuously improve the healthcare systems into their educational curriculum (Cronenwett, Sherwood, & Gelmon, 2009). Yet, the knowledge and skills included in QSEN are still mostly focusing on safety and not inclusive of all aspects of quality nursing care. Therefore, it is critical to examine what constitutes quality nursing care and set a clear professional standard to guide educational curriculum to create strong nurses who can not only practice safely, but embody nursing values in the QI of the current healthcare system.
When QI activities are aligned with nurses’ professional values, nurses, as providers of bedside care and professionals, can be a major force in making meaningful changes in quality improvement. As professionals who self-regulate with the pursuit of excellence, nurses have an ethical responsibility to achieve standards higher than the minimum requirement. To thrive as professionals while meeting demands for QI, nurses need to reflect on their professional values and ethical responsibilities, and identify what qualities they need to improve and how to improve them instead of passively accepting mandatory QI activities. This will help nurses refocus on the value of nursing and take more active roles to improve the quality of care.
The author would like to thank Drs. Judith G. Baggs, Kathleen A. Knafl, and Deborah Eldredge for their constructive inputs through the development of this manuscript.
Funding information: This study was funded by a National Institute of Nursing Research (F32NR010644) and Sigma Theta Tau Beta Psi Chapter Research Award. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institute of Nursing Research or the National Institutes of Health.
Early work of this manuscript was presented at International Centre for Nursing Ethics Conference 07/18/2008 at Yale University, New Heven, Connecticut, USA.
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