***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Abstract Objective : To evaluate the use of a computerised support system for decision making for implementing evidence based clinical guidelines for the management of asthma and angina in adults in primary care . Design : A before and after pragmatic cluster r and omised controlled trial utilising a two by two incomplete block design . Setting : 60 general practice s in north east Engl and . Participants : General practitioners and practice nurses in the study practice s and their patients aged 18 or over with angina or asthma . Main outcome measures : Adherence to the guidelines , based on review of case notes and patient reported generic and condition specific outcome measures . Results : The computerised decision support system had no significant effect on consultation rates , process of care measures ( including prescribing ) , or any patient reported outcomes for either condition . Levels of use of the software were low . Conclusions : No effect was found of computerised evidence based guidelines on the management of asthma or angina in adults in primary care . This was probably due to low levels of use of the software , despite the system being optimised as far as was technically possible . Even if the technical problems of producing a system that fully supports the management of chronic disease were solved , there remains the challenge of integrating the systems into clinical encounters where busy practitioners manage patients with complex , multiple conditions Background — Indirect evidence shows that alerting users with clinical decision support systems seems to change behavior more than requiring users to actively initiate the system . However , r and omized trials comparing these methods in a clinical setting are lacking . We studied the effect of both alerting and on-dem and decision support with respect to screening and treatment of dyslipidemia based on the guidelines of the Dutch College of General Practitioners . Methods and Results — In a clustered r and omized trial design , 38 Dutch general practice s ( 77 physicians ) and 87 886 of their patients ( 39 433 men 18 to 70 years of age and 48 453 women 18 to 75 years of age ) who used the ELIAS electronic health record participated . Each practice was assigned to receive alerts , on-dem and support , or no intervention . We measured the percentage of patients screened and treated after 12 months of follow-up . In the alerting group , 65 % of the patients requiring screening were screened ( relative risk versus control=1.76 ; 95 % confidence interval , 1.41 to 2.20 ) compared with 35 % of patients in the on-dem and group ( relative risk versus control=1.28 ; 95 % confidence interval , 0.98 to 1.68 ) and 25 % of patients in the control group . In the alerting group , 66 % of patients requiring treatment were treated ( relative risk versus control=1.40 ; 95 % confidence interval , 1.15 to 1.70 ) compared with 40 % of patients ( relative risk versus control=1.19 ; 95 % confidence interval , 0.94 to 1.50 ) in the on-dem and group and 36 % of patients in the control group . Conclusion — The alerting version of the clinical decision support systems significantly improved screening and treatment performance for dyslipidemia by general practitioners Abstract Objective : To underst and the factors influencing the adoption of a  computerised clinical decision support system  for two chronic diseases in general practice . Design : Practice based , longitudinal , qualitative interview study . Setting : Five general practice s in north east Engl and . Participants : 13 respondents ( two practice managers , three nurses , and eight general practitioners ) gave a total of 19 semistructured interviews . 40 people in practice s included in the r and omised controlled trial ( 34 doctors , three nurses ) and interview study ( three doctors , one previously interviewed ) gave feedback . Results : Negative comments about the decision support system significantly outweighed the positive or neutral comments . Three main areas of concern among clinicians emerged : timing of the guideline trigger , ease of use of the system , and helpfulness of the content . Respondents did not feel that the system fitted well within the general practice context . Experience of “ on-dem and ” information sources , which were generally more positively viewed , informed the comments about the system . Some general practitioners suggested that nurses might find the guideline content more clinical ly useful and might be more prepared to use a computerised decision support system , but lack of feedback from nurses who had experienced the system limited the ability to assess this . Conclusions : Significant barriers exist to the use of complex clinical decision support systems for chronic disease by general practitioners . Key issues include the relevance and accuracy of messages and the flexibility to respond to other factors influencing decision making in primary care . What is already known on this topic R and omised controlled trials of complex computerised decision support systems have found low rates of use and no effects on process and outcomes of care What this study adds Clinicians found a  computerised decision support system for chronic disease in general practice to be difficult to use and unhelpful clinical ly It did not fit well into a general practice consultation and compared unfavourably with “ on-dem and ” information “ Active ” decision support can make clinicians aware of gaps between their own practice and “ best ” practice , but computer prompts need to be relevant and Abstract Objectives : To investigate the effect of a computer based clinical decision support system and a risk chart on absolute cardiovascular risk , blood pressure , and prescribing of cardiovascular drugs in hypertensive patients . Design : Cluster r and omised controlled trial . Setting : 27 general practice s in Avon . Participants : 614 patients aged between 60 and 79 years with high blood pressure . Interventions : Patients were r and omised to computer based clinical decision support system plus cardiovascular risk chart ; cardiovascular risk chart alone ; or usual care . Main outcome measures : Percentage of patients in each group with a five year cardiovascular risk≥10 % , systolic blood pressure , diastolic blood pressure , prescribing of cardiovascular drugs . Results : Patients in the computer based clinical decision support system and chart only groups were no more likely to have cardiovascular risk reduced to below 10 % than patients receiving usual care . Patients in the computer based clinical decision support group were more likely to have a cardiovascular risk≥10 % than chart only patients , odds ratio 2.3 ( 95 % confidence interval 1.1 to 4.8 ) . The chart only group had significantly lower systolic blood pressure compared with the usual care group ( difference in means−4.6 mm Hg ( 95 % confidence interval−8.4 to−0.8 ) . Reduction of diastolic blood pressure did not differ between the three groups . The chart only group were twice as likely to be prescribed two classes of cardiovascular drugs and over three times as likely to be prescribed three or more classes of drugs compared with the other groups . Conclusions : The computer based clinical decision support system did not confer any benefit in absolute risk reduction or blood pressure control and requires further development and evaluation before use in clinical care can be recommended . Use of chart guidelines are associated with a potentially important reduction in systolic blood pressure Computers are steadily being incorporated in clinical practice . We conducted a nonr and omised , controlled , prospect i ve trial of electronic messages design ed to enhance adherence to clinical practice guidelines . We studied 126 physicians and nurse practitioners who used electronic medical records when caring for 349 patients with HIV infection in a primary care practice . We analysed the response times of clinicians to the situations that triggered alerts and reminders , the number of ambulatory visits , and hospitalisation . The median response times to 303 alerts in the intervention group and 388 alerts in the control group were 11 and 52 days ( p < 0.0001 ) , respectively . The median response time to 432 reminders in the intervention group was 114 days and that for 360 reminders in the control group was over 500 days ( p < 0.0001 ) . There was no effect on visits to the primary care practice . There was , however , a significant increase in the rate of visits outside the primary care practice ( p = 0.02 ) , which is explained by the increased frequency of visits to ophthalmologists . There were no differences in admission rates ( p = 0.47 ) , in admissions for pneumocystosis ( p = 0.09 ) , in visits to the emergency ward ( p = 0.24 ) , or in survival ( p = 0.19 ) . We conclude that the electronic medical record was effective in helping clinicians adhere to practice guidelines The majority of general practitioners in the Netherl and s have replaced traditional paper-based patient records with computer-based records ; physicians enter patient data themselves in the computer during patient encounters ( 1 ) . The use of electronic patient records creates new opportunities to influence physician behavior through implementation of decision support systems ( 2 - 7 ) . In recent years , research ers have documented various computer-based decision support systems that have influenced physician behavior ( 8 - 17 ) . Other investigators , however , have reported that computer-based decision support has not affected patient care ( 18 ) . To resolve the issue , investigators have compared the results of studies that were conducted in different setting s , used different methods , and involved different population s ( 19 ) . Studies comparing different methods of providing computer-based decision support in r and omized trials are not available . In the Netherl and s , 3 % to 4 % of patient encounters with general practitioners in primary care result in the ordering of blood tests ( 20 ) . However , ordering of blood tests is not always appropriate ( 21 - 29 ) . Research ers argue that excessive ordering of tests causes physicians to pursue evaluation of false-positive results , which in turn leads to additional unnecessary diagnostic examinations ( 30 - 35 ) . Two methods have proven effective in reducing the number of tests ordered by Dutch general practitioners . The first method is based on restricting the number of tests that are listed on an order form . Zaat and colleagues ( 36 , 37 ) developed a restricted paper order form that replaced the existing form . The second method involves introduction of indication-oriented order forms that are based on clinical practice guidelines ( 38 - 40 ) . We hypothesized that an indication-oriented order form based on guidelines , which would provide an optimally restricted list of tests that are relevant for a specific indication , would be more effective in decreasing the number of tests ordered compared with an order form that provides an initially limited list of tests . We therefore conducted a r and omized trial to compare the effect of two versions of BloodLink , a computer-based clinical decision support system , on blood test ordering  among Dutch general practitioners . Methods Participants In August and September 1995 , all 64 practice s ( 94 general practitioners ) in the region of Delft , the Netherl and s , were invited to participate in the study . Only practice s that had replaced their paper-based patient records with electronic records and were using the computer during patient encounters were eligible . A total of 46 practice s ( 62 general practitioners ) agreed to participate . R and omization To avoid contamination , we performed r and om allocation at the level of the practice ( 41 , 42 ) . The practice s were first stratified by type : solo practice s or group practice s ( two or more general practitioners in the same practice ) . Each practice was subsequently assigned by simple r and om allocation to use BloodLink-Restricted or BloodLink- Guideline for the full study period . A research er who was not involved in the study and was blinded to the identity of the practice s performed the r and omization by using a r and om-numbers table . After r and omization , 22 practice s involving 30 general practitioners were assigned to use BloodLink-Restricted  and 24 practice s involving 32 general practitioners were assigned to use BloodLink- Guideline  . Intervention We developed two versions of BloodLink , a computer-based decision support system . BloodLink-Restricted initially displays a reduced list of tests , whereas BloodLink- Guideline is based on the guidelines of the Dutch College of General Practitioners . Both versions of BloodLink are integrated with the computer-based patient record ( 43 ) . The option to use BloodLink was added to the screen that the general practitioner uses when entering data in the electronic patient record during patient encounters . The general practitioner can activate BloodLink to order blood tests as an alternative to using paper order forms . Because the total number of tests that can be ordered is too large to display on a computer screen , a set of tests is presented for selection . If the physician requires additional tests that are not currently displayed , he or she can type the first few letters of the names of the required tests , and the system will present all possible matches ( including those corresponding to possible typing errors of the general practitioner ) for selection . The number of tests that the general practitioners had at their disposal was the same both before and during the intervention ( 52 clinical chemistry tests and 46 microbiological tests ) . Options for specific instructions to the laboratory ( for example , urgent processing or fasting values ) are available . Once the physician has made his or her selection s , BloodLink prints a patient-specific test order form and instructions for the laboratory and up date s the patient record with the tests that have been ordered . The only difference between the two versions of BloodLink is the method used to present the initial set of tests to the general practitioner . BloodLink-Restricted is based on the idea of a restricted order form . It offers the general practitioner an initial set of 15 tests that have been shown to cover most of the clinical situations seen in primary care ( 36 ) . BloodLink-Restricted can be viewed as a general electronic order form that presents only 15 tests on the screen , together with a field labeled other tests that allows the physician to request any other blood test ( 43 ) . The 15 tests are alanine aminotransferase , aspartate aminotransferase , total bilirubin , cholesterol , creatinine , erythrocyte sedimentation rate , free thyroxine , -glutamyltransferase , glucose ( and fasting glucose ) , glycosylated hemoglobin , hemoglobin , mean corpuscular volume , PaulBunnell , potassium , and thyroid-stimulating hormone . At any time , the physician can customize tests for individual patients by adding or deleting tests . BloodLink- Guideline is based on the guidelines of the 
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Conclusions There is little evidence at the moment for the effectiveness of an increasingly used and commercialised instrument such as electronic multidimensional guidelines .
This conclusion implies a considerable risk towards inappropriate investments in ineffective implementation interventions and in suboptimal care