Monitoring patients using control charts: a systematic review.

Ruth Tennant, Mohammed Mohammed, Jamie Coleman, Una Martin

Research output: Contribution to journalArticle

44 Citations (Scopus)


OBJECTIVE: To systematically review the uses control charts to monitor clinical variables in individual patients. DATA SOURCES: Systematic searches of MEDLINE, CINAHL, Embase and five other databases yielded 74 studies, of which seven met our inclusion criteria of using control charts to monitor clinical variables for disease at an individual patient level. REVIEW METHODS: Included articles were reviewed independently by two reviewers. Data were extracted on study design clinical condition or disease being monitored, clinical variable or marker, measurement method, outcome measure and any changes in clinical indicator identified in the articles. RESULTS: Control charts were applied to four conditions--hypertension, asthma, renal function post-transplant and diabetes. Studies fell into two categories. Three studies sought to determine the 'performance' of control charts in comparison with existing 'gold standard methods' in terms of sensitivity and specificity based on moderate sample sizes (n = 35-45). This category of studies found control charts to be simple, low-cost, effective tools with good sensitivity and specificity characteristics and concluded in favour of control charts. The other four studies were individual patient case-studies in which the use of control charts to monitor clinical variables was associated with a positive impact on patient and carer experience albeit anecdotally and with varying degrees of attention. CONCLUSIONS: Control charts appear to have a promising but largely under-researched role in monitoring clinical variables in individual patients. Furthermore, rigorous evaluation of control charts is required.
Original languageEnglish
Pages (from-to)187-94
Number of pages8
JournalInternational Journal for Quality in Health Care
Issue number4
Publication statusPublished - 1 Aug 2007


  • random variation
  • statistical process control
  • variation
  • chronic diseases
  • quality monitoring
  • quality of care


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