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Make Your Data Work For You

Summary: Just as cash flow is the lifeblood of any business, data form the basic currency of improvement work. No data, no improvement. Data collection is not usually an issue; many organizations collect enormous amounts of data. However, the inability to use the available data for improvement remains a major challenge for many organizations. Several factors account for this, but the end result is inevitably the same: a huge waste of staff time and effort.

Many healthcare organizations collect data, quantitative or qualitative, for various reasons, e.g. regulatory requirements, hospital accreditation, benchmarking, nursing and infection surveillance, customer satisfaction surveys, “we’ve been doing this for the last 15 years (why stop?)”. Then why is there overall so little improvement, in terms of clinical outcomes, patient safety, operational improvement, patient satisfaction, and financial results?

What is the Underlying Problem?

Here are five suggestions on where the problem(s) might lie:

  1. Strategic quality planning. Quality should be a significant component of any healthcare organization’s strategic planning. Failure to plan = Planning to fail. Quality initiatives should be aligned with the organization’s strategic goals, and will determine, to a large extent, the data to be collected. Quality planning also entails ensuring a communication structure for improvement data and a framework for action, i.e. what to do with the data.
  2. Quality of the quality data. Actionable data are data that leaders and improvement teams can use to make appropriate decisions. Data need to be complete, accurate (valid), consistent, relevant, timely, and reliable for them to be used for improvement. Failure to obtain high-quality data indicates a problem with skills, leadership and/or the data management process.
  3. Transforming data into information. Many organizations find themselves data rich but information poor. A system (human or computer-automated) to efficiently transform data into actionable information for decision-making is necessary. In our experience, many organizations fail to adequately empower their staff with the skills to effectively transform data into useful information.
  4. Improvement skills. Often, staff do not possess the basic skills needed to engage in improvement efforts, e.g. knowledge of improvement methodology, measurement and financial literacy, and goal setting.
  5. Top management support. Reticence on the part of decision makers in the face of valid, reliable, timely, and well-presented data is common and a serious issue. Lack of top management support in improvement work rates as one of the biggest demotivators for teams and a main reason for failure of an organization to improve.

Data collection in healthcare quality is something like joining a gym. One might join the gym to boost one’s fitness, and to look and feel better. However, if one then fails to work out at the gym regularly as part of a pre-determined fitness programme, one is unlikely to see the desired results. Similarly, if a hospital embarks on data collection for improvement, but does not capitalize on the data for any of the reasons listed above, it will make the exercise merely busy work, and the desired results will almost certainly not be forthcoming.

In these challenging economic times, and with rising competition and more educated consumers of health care, quality data—often the product of much staff time and effort—are things that most hospitals can ill afford to waste.

Comments on this entry are closed.

  • ALABI June 21, 2011, 8:16 pm

    are there a list of requisite skills or knowledge needed to transform data to information?

    • Andy Teh June 22, 2011, 12:22 pm

      @ALABI—Relevant clinical knowledge, adequate statistical skills, experience, common sense, and communication skills are required to collect, display/describe, analyze, and interpret data, and to communicate findings. This is a basic skill set for any competent healthcare quality professional. Of the various competencies needed, I think many people struggle with statistics, e.g. statistical process control (SPC).