***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 Many believe that medical expert systems have great potential to improve health care , but few of these systems have been rigorously evaluated , and even fewer are in routine use . We propose the evaluation of  medical expert systems  in two stages : laboratory and field testing . In the former , the perspectives of both prospect i ve users and experts responsible for implementation are valuable . In the latter , the study must be design ed to test , in an unbiased manner , whether the system is used in clinical practice , and if it is used , how it affects the structure , process and outcome of health care encounters . We conclude with proposals for encouraging the objective evaluation of these systems 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 OBJECTIVE A research prototype Physician Workstation ( PWS ) incorporating a graphical user interface and a drug ordering module was compared with the existing hospital information system in an academic Veterans Administration General Medical Clinic . Physicians in the intervention group received recommendations for drug substitutions to reduce costs and were alerted to potential drug interactions . The objective was to evaluate the effect of the PWS on user satisfaction , on health-related outcomes , and on costs . DESIGN A one-year , two-period , r and omized controlled trial with 37 subjects . MEASUREMENTS Differences in the reliance on noncomputer sources of information , in user satisfaction , in the cost of prescribed medications , and in the rate of clinical ly relevant drug interactions were assessed . RESULTS The study subjects logged onto the workstation an average of 6.53 times per provider and used it to generate 2.8 % of prescriptions during the intervention period . On a five-point scale ( 5 = very satisfied , 1 = very dissatisfied ) , user satisfaction declined in the PWS group ( 3.44 to 2.98 p = 0.008 ) , and increased in the control group ( 3.23 to 3.72 , p < 0.0001 ) . CONCLUSION The intervention physicians did not use the PWS frequently enough to influence information-seeking behavior , health outcomes , or cost . The study design did not determine whether the poor usage result ed from satisfaction with the control system , problems using the PWS intervention , or the functions provided by the PWS intervention . Evaluative studies should include provisions to improve the chance of successful implementation as well as to yield maximum information if a negative study occurs 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 BACKGROUND Although they are effective in outpatient setting s , computerized reminders have not been proved to increase preventive care in inpatient setting s. METHODS We conducted a r and omized , controlled trial to determine the effects of computerized reminders on the rates at which four preventive therapies were ordered for in patients . During an 18-month study period , a computerized system processed on-line information for all 6371 patients admitted to a general-medicine service ( for a total of 10,065 hospitalizations ) , generating preventive care reminders as appropriate . Physicians who were in the intervention group viewed these reminders when they were using a computerized order-entry system for in patients . RESULTS The reminder system identified 3416 patients ( 53.6 percent ) as eligible for preventive measures that had not been ordered by the admitting physician . For patients with at least one indication , computerized reminders result ed in higher adjusted ordering rates for pneumococcal vaccination ( 35.8 percent of the patients in the intervention group vs. 0.8 percent of those in the control group , P<0.001 ) ,  influenza vaccination ( 51.4 percent vs. 1.0 percent , P < 0.001 ) , prophylactic heparin ( 32.2 percent vs. 18.9 percent , P<0.001 ) , and prophylactic aspirin at discharge ( 36.4 percent vs. 27.6 percent , P<0.001 ) . CONCLUSIONS A majority of hospitalized patients in this study were eligible for preventive measures , and computerized reminders significantly increased the rate of delivery of such therapies 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 
    Output:
    
Use of a CBPRS was perceived favourably by physicians , with studies of satisfaction being mainly positive .
The 12 studies evaluating the impact on medical practice and guidelines compliance showed that positive experiences were as frequent as experiences showing no benefit .
CBPRS increased user and patient satisfaction , which might lead to significant improvements in medical care practice s. However , the studies on the impact of CBPRS on patient outcomes and quality of care were not conclusive .