Computer-assisted versus oral-and-written family history taking for identifying people with elevated risk of type 2 diabetes mellitus.

BACKGROUND Diabetes is a chronic illness characterised by insulin resistance or deficiency, resulting in elevated glycosylated haemoglobin A1c (HbA1c) levels. Because diabetes tends to run in families, the collection of data is an important tool for identifying people with elevated risk of type2 diabetes. Traditionally, oral-and-written data collection methods are employed but computer-assisted history taking systems (CAHTS) are increasingly used. Although CAHTS were first described in the 1960s, there remains uncertainty about the impact of these methods on family history taking, clinical care and patient outcomes such as health-related quality of life.  OBJECTIVES To assess the effectiveness of computer-assisted versus oral-and-written family history taking for identifying people with elevated risk of developing type 2 diabetes mellitus. SEARCH METHODS We searched The Cochrane Library (issue 6, 2011), MEDLINE (January 1985 to June 2011), EMBASE (January 1980 to June 2011) and CINAHL (January 1981 to June 2011). Reference lists of obtained articles were also pursued further and no limits were imposed on languages and publication status. SELECTION CRITERIA Randomised controlled trials of computer-assisted versus oral-and-written history taking in adult participants (16 years and older). DATA COLLECTION AND ANALYSIS Two authors independently scanned the title and abstract of retrieved articles. Potentially relevant articles were investigated as full text. Studies that met the inclusion criteria were abstracted for relevant population and intervention characteristics with any disagreements resolved by discussion, or by a third party. Risk of bias was similarly assessed independently. MAIN RESULTS We found no controlled trials on computer-assisted versus oral-and-written family history taking for identifying people with elevated risk of type 2 diabetes mellitus. AUTHORS' CONCLUSIONS There is a need to develop an evidence base to support the effective development and use of computer-assisted history taking systems in this area of practice. In the absence of evidence on effectiveness, the implementation of computer-assisted family history taking for identifying people with elevated risk of type 2 diabetes may only rely on the clinicians' tacit knowledge, published monographs and viewpoint articles.

[1]  J. Bachman,et al.  The patient-computer interview: a neglected tool that can aid the clinician. , 2003, Mayo Clinic proceedings.

[2]  P. Tang,et al.  Clinician information activities in diverse ambulatory care practices. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[3]  M. Pringle,et al.  Preventing ischaemic heart disease in one general practice: from one patient, through clinical audit, needs assessment, and commissioning into quality improvement , 1998, BMJ.

[4]  N. Clark,et al.  Standards of Medical Care in Diabetes: Response to Power , 2006 .

[5]  Michael M. Wagner,et al.  Review: Accuracy of Data in Computer-based Patient Records , 1997, J. Am. Medical Informatics Assoc..

[6]  W. Reitsma,et al.  [WHO Expert Committee on diabetes mellitus]. , 1981, Nederlands tijdschrift voor geneeskunde.

[7]  Jonathan A C Sterne,et al.  Systematic reviews in health care: Investigating and dealing with publication and other biases in meta-analysis. , 2001, BMJ.

[8]  Mark Benaroia,et al.  Patient-directed intelligent and interactive computer medical history-gathering systems: A utility and feasibility study in the emergency department , 2007, Int. J. Medical Informatics.

[9]  I. Olkin,et al.  The case of the misleading funnel plot , 2006, BMJ : British Medical Journal.

[10]  赵红彬 Placebo , 2007 .

[11]  P. Quinn,et al.  Assessment of an electronic daily diary in patients with overactive bladder , 2003, BJU international.

[12]  Jaya,et al.  Differences in young people's reports of sexual behaviors according to interview methodology: a randomized trial in India. , 2008, American journal of public health.

[13]  H. Llewelyn Challenges to implementing NPfIT: Computerised medical history is key to connecting health , 2005, BMJ : British Medical Journal.

[14]  K. Shojania,et al.  The tension between needing to improve care and knowing how to do it. , 2007, The New England journal of medicine.

[15]  P. Raskin,et al.  Report of the expert committee on the diagnosis and classification of diabetes mellitus. , 1999, Diabetes care.

[16]  Donna C. Dare,et al.  Reasons provided by prescribers when overriding drug-drug interaction alerts. , 2007, The American journal of managed care.

[17]  P. Shekelle,et al.  Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care , 2006, Annals of Internal Medicine.

[18]  Robyn Tamblyn,et al.  Review Paper: The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systematic Review , 2005, J. Am. Medical Informatics Assoc..

[19]  J. Ioannidis,et al.  The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration , 2009, Annals of Internal Medicine [serial online].

[20]  V. Preedy,et al.  Randomized Controlled Trial , 2010 .

[21]  放射線影響研究所 Technical report series , 1989 .

[22]  David G Steel,et al.  Computerized dietary assessments compare well with interviewer administered diet histories for patients with type 2 diabetes mellitus in the primary healthcare setting. , 2008, Patient education and counseling.

[23]  Ramesh Farzanfar,et al.  humanization of health care technology When computers should remain computers : a qualitative look at the , 2006 .

[24]  A. Localio,et al.  Role of computerized physician order entry systems in facilitating medication errors. , 2005 .

[25]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[26]  J. Beaumont,et al.  Familial risk for common diseases in primary care: the Family Healthware Impact Trial. , 2009, American journal of preventive medicine.

[27]  Shannon J. Lane,et al.  Bmc Medical Informatics and Decision Making a Review of Randomized Controlled Trials Comparing the Effectiveness of Hand Held Computers with Paper Methods for Data Collection , 2006 .

[28]  P. Tang,et al.  Methods for assessing information needs of clinicians in ambulatory care. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[29]  J. Sidorov It Ain't Necessarily So: The Electronic Health Record And The Unlikely Prospect Of Reducing Health Care Costs. , 2006, Health affairs.

[30]  O. Dale,et al.  Despite technical problems personal digital assistants outperform pen and paper when collecting patient diary data. , 2007, Journal of clinical epidemiology.

[31]  James M. Galliher,et al.  Data Collection Outcomes Comparing Paper Forms With PDA Forms in an Office-Based Patient Survey , 2008, The Annals of Family Medicine.

[32]  D. Altman,et al.  Measuring inconsistency in meta-analyses , 2003, BMJ : British Medical Journal.

[33]  A. Bowling Mode of questionnaire administration can have serious effects on data quality. , 2005, Journal of public health.

[34]  P. Zimmet,et al.  Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation , 1998, Diabetic medicine : a journal of the British Diabetic Association.

[35]  T. Vos,et al.  Cost-Effectiveness of Interventions to Promote Physical Activity: A Modelling Study , 2009, PLoS medicine.

[36]  S. Thompson,et al.  Quantifying heterogeneity in a meta‐analysis , 2002, Statistics in medicine.

[37]  George Wolford,et al.  A clinical trial comparing interviewer and computer-assisted assessment among clients with severe mental illness. , 2008, Psychiatric services.

[38]  S. Wild,et al.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. , 2004, Diabetes care.