Evaluation of computer based clinical decision support system and risk chart for management of hypertension in primary care: randomised controlled trial ‘Decision support system (DSS) for prevention of cardiovascular disease (CVD) among hypertensive (HTN) patients in Andhra Pradesh, India’ – a cluster randomised community intervention trial Computerized decision support based on a clinical practice guideline improves compliance with care standards. The Use of a Computer-Based Decision Support System Facilitates Primary Care Physicians’ Management of Chronic Pain Randomised controlled trial of computer-held medical records in hypertensive patients. A computer expert system prototype for mechanically ventilated neonates development and impact on clinical judgment and information access capability of nurses. Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial Implementing Antibiotic Practice Guidelines through Computer-Assisted Decision Support: Clinical and Financial Outcomes A taxonomic description of computer-based clinical decision support systems Subjective usability of the CARDSS guideline-based decision support system Implementing clinical guidelines in the treatment of hypertension in general practice. Evaluation of patient outcome related to implementation of a computer-based clinical decision support system. The GRAIDS Trial: a cluster randomised controlled trial of computer decision support for the management of familial cancer risk in primary care Effect of computer support on younger women with breast cancer Research Paper: A Randomized Trial of the Effectiveness of On-demand versus Computer-triggered Drug Decision Support in Primary Care A computerized handheld decision-support system to improve pulmonary embolism diagnosis: a randomized trial. Implementation and evaluation of a decision support system for type II diabetes. Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial Lessons from a randomized controlled trial designed to evaluate computer decision support software to improve the management of asthma. Effect of computer-aided management on the quality of treatment in anticoagulated patients: a prospective, randomized, multicenter trial of APROAT (Automated PRogram for Oral Anticoagulant Treatment). Cluster-randomized, controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke. The Effects of a Special Computer Network on Caregivers of Persons with Alzheimer's Disease Application of Information Technology: Handheld Computer-based Decision Support Reduces Patient Length of Stay and Antibiotic Prescribing in Critical Care Computer support for interpreting family histories of breast and ovarian cancer in primary care: comparative study with simulated cases
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 Background Very few studies having decision support systems as an intervention report on patient outcomes for cardiovascular disease in the Western world . The potential role of decision support system for the management of blood pressure among Indian hypertensives remains unclear . We propose a cluster r and omised trial that aims to test the effectiveness and cost effectiveness of DSS among Indian hypertensive patients . Methods The trial design is a cluster r and omised community intervention trial , in which the participants would be adult male and female hypertensive patients , in the age group of 35 to 64 years , reporting to the Primary Health Care centres of Mahabubnagar district , And hra Pradesh , India . The objective of the study is to test the effectiveness and compare the cost effectiveness and cost utility among hypertensive subjects r and omized to receive either decision support system or a chart based algorithmic support system in urban and rural areas of a district in the state of And hra Pradesh , India ( baseline versus 12 months follow up ) . The primary outcome would be a comparison of the systolic blood pressure at 0 and 12 months among hypertensive patients r and omized to receive the decision support system or the chart based algorithmic support system . Computer generated r and omisation and an investigator and analyser blinded method would be followed . 1600 participants ; 800 to each arm ; each arm having eight clusters of hundred participants each have been recruited between 01 August 2011 - 01 March 2012 . A twelve month follow up will be completed by March 2013 and results are expected by April 2013 . Discussion This cluster r and omized community intervention trial on DSS will enable policy makers to find out the effectiveness , cost effectiveness and cost utility of decision support system for management of blood pressure among hypertensive patients in India . Most of the previous studies on decision support system have focused on physician performance , adherence and on preventive care reminders . The uniqueness of the proposed study lies in finding out the effectiveness of a decision support system on patient related outcomes .Trial registration CTRI/2012/03/002476 , Clinical Trial Registry - India PURPOSE Clinical guidelines are design ed to assist in the management of specific diseases ; however , these guidelines are often neglected in the delivery of care . The purpose of this study was to determine whether clinician use of an clinical practice guideline would increase in response to having , at the patient visit , a decision support system based on a practice guideline that generates a customized management protocol for the individual patient using data from the patient 's electronic medical record . SUBJECTS AND METHODS In a 6-month controlled trial at a primary care clinic , 58 primary care clinicians were r and omized to receive either a special encounter form with the computer-generated guideline recommendations or a st and ard encounter form . The effect of computer-generated advice on clinician behavior was measured as rate of compliance with guideline recommendations . Data from 30 clinicians were analyzed ; data from 28 clinicians were excluded because these clinicians did not meet predefined criteria for minimum exposure to diabetic patient care . RESULTS Availability of patient management recommendations generated by the decision support system result ed in a two-fold increase in clinician compliance with care guidelines for diabetes mellitus ( P = 0.01 ) . Median compliance for the group receiving the recommendations was 32.0 % versus 15.6 % for the control group . CONCLUSION Decision support based on a clinical practice guideline is an effective tool for assisting clinicians in the management of diabetic patients . This decision support system provides a model for how a clinical practice guideline can be integrated into the care process by computer to assist clinicians in managing a specific disease through helping them comply with care st and ards . Use of decision support systems based on clinical practice guidelines could ultimately improve the quality of medical care We tested whether computer-based decision support ( CBDS ) could enhance the ability of primary care physicians ( PCPs ) to manage chronic pain . Structured summaries were generated for 50 chronic pain patients referred by PCPs to a pain clinic . A pain specialist used a decision support system to determine appropriate pain therapy and sent letters to the referring physicians outlining these recommendations . Separately , five board-certified PCPs used a CBDS system to “ treat ” the 50 cases . A successful outcome was defined as one in which new or adjusted therapies recommended by the software were acceptable to the PCPs ( i.e. , they would have prescribed it to the patient in actual practice ) . Two pain specialists review ed the PCPs ’ outcomes and assigned medical appropriateness scores ( 0 = totally inappropriate to 10 = totally appropriate ) . One year later , the hospital data base provided information on how the actual patients ’ pain was managed and the number of patients re-referred by their PCP to the pain clinic . On the basis of CBDS recommendations , the PCP subjects “ prescribed ” additional pain therapy in 213 of 250 evaluations ( 85 % ) , with a medical appropriateness score of 5.5 ± 0.1 . Only 25 % of these chronic pain patients were subsequently re-referred to the pain clinic within 1 yr . The use of a CBDS system may improve the ability of PCPs to manage chronic pain and may also facilitate screening of consults to optimize specialist utilization A total of 278 hypertensive patients in three clinics were r and omly allocated to have their medical records held in a computer system ( 136 ) or on st and ard hospital notes ( 142 ) . For the computer system the doctor completed a structured input form , and the information on symptoms , physical findings , and diagnoses was more complete than that in the st and ard notes . This result ed in certain symptoms and risk factors being recognised more often when the computer system was used . The hypertension clinics ' routines were not disrupted by the introduction of a computer-held system , and follow-up consultation times were not affected by the type of records kept , although the first consultation took eight minutes longer when computer documents were completed . The patients remained in the trial for one year and clinical management was assessed from blood pressure control , drop-out rates , and the frequency of performing investigations . These estimates of management showed no significant difference between the two groups , but the attempt to tailor the computer system to help management made the system acceptable to the doctors using it . The computer system continues to be used and is providing data for research into hypertension A computer expert system is an alternative method of training and providing real-time clinical decision support for nurses to advance their practice s from a novice to a proficient level . The purpose of this study was twofold : ( 1 ) to develop a prototype of a computer expert system for mechanically ventilated neonates ( ES-MVN ) and ( 2 ) to assess the impact of the ES-MVN on the clinical judgment and information access capability of nurses . Five steps used in developing the prototype are described in this article . The ES-MVN is a multimedia interactive consultation-based program that contains 2 major parts : the nursing diagnosis and the knowledge base on nursing care of mechanically ventilated neonates . A rule-based ( Boolean frame ) was chosen for the nursing diagnosis decision model . The prototype was developed on the Web server and a combination of computer applications operated in a Microsoft Windows environment . A quasi-experimental , 1-group pretest-posttest design was used to measure the efficacy of the  ES-MVN  in 16 neonatal intensive care unit registered nurses . Case simulations were used to test the nurses ' clinical judgment performance . The results showed a significant increase in the nurses ' performance scores of diagnoses and managed care after using the ES-MVN ( t[15 ] = 17.21 , P = .0001 ) . The nurses ' scores for perceptions of their information access capability and clinical judgment ability after receiving the ES-MVN were significantly higher than before installing the ES-MVN in the neonatal intensive care unit ( ts[15 ] = 6.91 and = 17.53 , Ps = .0001 , respectively ) . The findings suggest the usefulness of the computer expert system as an effective tool to support nurses ' clinical judgment in critical care situations and to provide access to information at the practice site OBJECTIVE The use of evidence -based guidelines can improve the care for asthma patients . We implemented a computerized asthma management system in a pediatric emergency department ( ED ) to integrate national guidelines . Our objective was to determine whether patient eligibility identification by a probabilistic disease detection system ( Bayesian network ) combined with an asthma management system embedded in the workflow decreases time to disposition decision . METHODS We performed a prospect i ve , r and omized controlled trial in an urban , tertiary care pediatric ED . All patients 2 - 18 years of age presenting to the ED between October 2010 and February 2011 were screened for inclusion by the disease detection system . Patients identified to have an asthma exacerbation were r and omized to intervention or control . For intervention patients , asthma management was computer-driven and workflow-integrated including computer-based asthma scoring in triage , and time-driven display of asthma-related reminders for re-scoring on the electronic patient status board combined with guideline -compliant order sets . Control patients received st and ard asthma management . The primary outcome measure was the time from triage to disposition decision . RESULTS The Bayesian network identified 1339 patients with asthma exacerbations , of which 788 had an asthma diagnosis determined by an ED physician-established reference st and ard ( positive predictive value 69.9 % ) . The median time to disposition decision did not differ among the intervention ( 228 min ; IQR=(141 , 326 ) ) and control group ( 223 min ; IQR=(129 , 316 ) ) ; ( p=0.362 ) . The hospital admission rate was unchanged between intervention ( 25 % ) and control groups ( 26 % ) ; ( p=0.867 ) . ED length of stay did not differ among intervention ( 262 min ; IQR=(165 , 410 ) ) and control group ( 247 min ; IQR=(163 , 379 ) ) ; ( p=0.818 ) . CONCLUSIONS The control and intervention groups were similar in regards to time to disposition ; the computerized management system did not add additional wait time . The time to disposition decision did not change ; however the management system integrated several different information systems to support clinicians ' communication Physicians ' decisions control between 70 % and 80 % of all health care dollars spent [ 1 - 3 ] , and many strategies to influence or control physician decision making have been advocated . These strategies include education , peer review with feedback , administrative interventions , financial incentives and penalties , critical pathways , and , most recently , nationally derived guidelines [ 2 , 4 ] . To date , none of these strategies has been clearly effective [ 4 ] . Berwick [ 5 ] has outlined the inherent flaws in many of these strategies . He concedes that these methods may lead to predictable care but notes that they can not lead to continual improvement of care . Nowhere in health care are these strategies to control or influence physicians more prevalent than in the area of drug use , particularly use of antimicrobial agents [ 6 ] . The hospital-wide use of drugs and the involvement of various health care providers create a system of diffuse responsibility , enormous variation , and escalating costs [ 6 - 9 ] . The United States currently spends $ 40 billion annually on pharmaceuticals ; this is 8 % of the total cost of health care [ 3 , 7 - 9 ] . Prescription drugs now constitute between 5 % and 20 % of an individual hospital 's total budget [ 7 ] . Antimicrobial agents are one of the costliest categories of drug expenditures in hospitals , accounting for approximately 20 % to 50 % of total spending on drugs [ 9 - 14 ] . Investigations in various clinical practice setting s have indicated that as much as 50 % of antibiotic use is inappropriate [ 14 - 17 ] . The consequences of this have been addressed in terms of antimicrobial resistance [ 18 , 19 ] , adverse drug reactions [ 15 , 17 ] , and cost [ 11 - 14 ] . In response to these pressures , professional societies and individual investigators have outlined methods with which to improve antibiotic use [ 20 - 29 ] . Most of these methods ( for example , drug formularies ) use some form of a control mechanism , and , to date , experience with them has been mixed [ 11 , 16 , 25 , 27 , 28 ] . Kassirer [ 30 ] has challenged the health care system to develop strategies that inform rather than enforce or control medical decisions . For more than a decade , we have been developing and investigating clinical management programs that augment and inform clinical decision making , in addition to focusing on continual quality improvement [ 31 , 32 ] , in antibiotic therapy , infection control surveillance , and the safety of drug use . These programs were design ed to provide continuous surveillance and computer-assisted decision support [ 33 , 34 ] to all clinicians responsible for inpatient care in a general hospital . The hallmark of these computer-assisted decision support programs was local clinician-derived consensus practice guidelines [ 5 , 31 , 34 , 35 ] that were programmed into a hospital information system as rules , algorithms , and predictive models . These programs managed antibiotic use at three basic levels : prophylactic use , empiric use , and therapeutic use . We review the clinical and process outcomes and the financial effects of these hospital-wide decision support programs during a 7-year period . Methods LDS Hospital , located in Salt Lake City , Utah , is a 520-bed private , community , acute-care referral hospital that serves as a teaching facility for the University of Utah Schools of Medicine , Nursing , and Pharmacy . The hospital provides most clinical services but not general pediatric care . An integrated , clinical ly oriented hospital information system has been under development at the institution for more than 20 years [ 36 ] . This system routinely collects and stores all patient data from multiple sources throughout the hospital . The system currently serves as the hospital 's clinical computing system , providing clinical information management and establishing computer-based patient records . The computer-based patient record contains both clinical and financial data . The financial data are derived from a st and ard cost-manager microcomputer software system that is linked to the clinical information system [ 37 , 38 ] . The information system also provides online clinical decision support through its expert system capabilities . Infectious diseases surveillance and therapeutics was the first medical domain to use the expert system features of the hospital information system on a widespread clinical basis [ 39 ] . The clinical decision support systems and the implementation methods for this domain were developed , tested , and implemented by clinical investigators in the Division of Infectious Diseases at LDS Hospital [ 37 - 52 ] . The process used to develop the local consensus guidelines for antimicrobial use was similar to the approach described by East and colleagues [ 34 ] . Our approach also included thorough evaluations of published reports , use of national guidelines and local expert opinion , and exhaustive analyses of the LDS Hospital patient data base ; we subsequently developed step-wise logistic regression models [ 48 , 49 ] . Through various committee representations , we also frequently consulted the medical staff of LDS Hospital ; in these consultations , we presented data and interim results . Using the aforementioned formal techniques [ 34 , 35 ] , the staff also helped develop , test , and implement the clinical practice guidelines that were embedded in the decision support programs . The practice guidelines were encoded into the knowledge base of the hospital information system as rules , algorithms , and predictive models . This allowed for decision support at the point of care , with feedback to physicians in real time . Thus , guideline application was patient specific , and recommendations corresponded to actual clinical conditions at a particular point in time . Feedback to physicians was open looped [ 53 ] , and the physicians ultimately decided whether or not to follow the recommendations . Since 1985 , many of these clinical decision support programs and guidelines have been prospect ively developed and tested in the patient population s of LDS Hospital , often in r and omized studies . Decision support programs have been systematic ally exp and ed to include comprehensive , institution-wide antibiotic management programs . These decision support programs were design ed to comprehensively manage all antibiotic agents used in the institution throughout the continuum of hospital care : 1 ) prophylactic [ surgical ] antibiotic use ; 2 ) empiric antibiotic use [ for suspected infection without microbiological data ] ; and 3 ) therapeutic antibiotic use ( for established infection with microbiological data ) . These programs continually track and assist physicians in managing each patient treated with an antibiotic at LDS Hospital and in all aspects of antibiotic use ; no antibiotic can be prescribed at LDS Hospital without being affected by these decision support programs . The methods used in these programs have been described elsewhere [ 37 - 52 ] . These programs are continually up date d as medical knowledge and the health care delivery system change , both locally and nationally . The surgical prophylactic decision support programs were developed with our surgical colleagues and result ed in strategies that ensured appropriate case selection , delivery time , intra-operative dosing , and duration of antibiotic use rather than solely concentrating on the specific antibiotic agent or class of agents for each surgical procedure [ 41 , 42 , 45 ] . The empiric and therapeutic antibiotic decision support programs provide information to the clinician in the form of computer-generated alerts or suggestions on the following : the presence of resistant pathogens ; untreated infections ; an incorrect dose , route , or interval of an antibiotic ; the absence of current renal function data ; the need for serum drug levels ; population -based probabilities of infections in relation to specific patient variables ; and cost-effective alternatives ( for example , oral therapy or narrower-spectrum agents ) [ 43 , 48 , 49 ] . Furthermore , these management programs monitor patients for excessive or suboptimal antibiotic doses , depending on the patients ' current renal function status [ 46 , 47 ] , and they address the prevention , early detection , and archiving of adverse drug events associated with these agents [ 44 , 46 , 50 ] . All but one of the computer-assisted antibiotic decision support programs described were in clinical use throughout the study period ; the exception was the adverse drug event program , which has been used since 1989 . Beginning in 1985 , investigators in the infectious disease division developed data base analysis programs that quantify antibiotic use and expenditures , identify prescriber and diagnosis-related groups for patients receiving antibiotics , track antibiotic resistance patterns , and distinguish therapeutic from prophylactic use of antibiotics . The reports generated by these data base analysis programs summarize antibiotic use by specific agent and place them in the following categories : numbers of patients treated , total milligrams administered , total doses administered , defined daily doses per 100 occupied bed-days [ 12 , 13 , 54 ] , and total amount spent . We used the number of defined daily doses per 100 occupied bed-days because it is a st and ardized technical unit of measurement that estimates drug use . A defined daily dose is based on the average adult maintenance dose ( usually in grams ) for the primary indication of the drug and is adjusted per 100 occupied bed-days . The concept of the defined daily dose per 100 occupied bed-days was established by a joint project of the Nordic Council on Medicines and the World Health Organization Center for Drug Collaboration Statistics [ 12 , 13 , 54 ] . Because the defined daily dose per 100 occupied bed-days is independent of cost ( which eliminates confounding introduced by the buying practice s of group purchasing organizations ) and differences in dose forms , it establishes a st and ardized basis for comparing drug use . The World Health Organization has agreed that the defined daily dose method of analysis can be OBJECTIVE Computer-based clinical decision support systems ( CDSSs ) vary greatly in design and function . Using a taxonomy that we had previously developed , we describe the characteristics of CDSSs reported in the literature . METHODS We search ed PubMed and the Cochrane Library for r and omized controlled trials ( RCTs ) published in English between 1998 and 2003 that evaluated CDSSs .  We coded each CDSS using our taxonomy . RESULTS 58 studies met our inclusion criteria . The 74 reported CDSSs varied greatly in context of use , knowledge and data sources , nature of decision support offered , information delivery , and workflow impact . Two distinct subsets of CDSSs were seen : patient-directed systems that provided decision support for preventive care or health-related behaviors via mail or phone ( 38 % of systems ) , and inpatient systems targeting clinicians with online decision support and direct online execution of the recommendations ( 18 % ) . 84 % of the CDSSs required extra staffing for h and ling CDSS-related input or output . CONCLUSION Reported CDSSs are heterogeneous along many dimensions . Caution should be taken in generalizing the results of CDSS RCTs to different clinical or workflow setting Clinical decision support systems ( CDSSs ) differ from other health information systems in their aim to directly influence the decision-making behaviour of healthcare professionals . As a result , CDSSs face additional challenges with respect to user acceptance . The objective of this study was to investigate subjective usability of a guideline -based CDSS for outpatient cardiac rehabilitation . The system , named CARDSS , was previously found to be effective in improving guideline adherence of rehabilitation professionals in a cluster r and omized trial . To assess  CARDSS ' usability , a modified version of the IBM Computer System Usability Question naire was sent to all 68 professionals from the 28 outpatient clinics that participated in the trial . The question naire was returned by 63 respondents ( 93 % ) from 27 clinics . Factors that influenced  CARDSS ' usability were identified using linear regression analysis . Analysis showed that professionals who managed to smoothly integrate the system with their daily routine were more satisfied with ease of system use . Furthermore , a positive attitude of respondents towards CDSSs in general and a better agreement with the content of the national guidelines were positively correlated to satisfaction with CARDSS ' overall usability and each of its sub-domains OBJECTIVE To evaluate the implementation of clinical guidelines for hypertension in general practice by use of a computer-based clinical decision support system ( CDSS ) and a specific implementation strategy . Evaluation of patient outcome . DESIGN R and omised study with health centres as units . The intervention group had the CDSS installed and made ready for use , doctors and assistants were trained and received a user-manual , the doctors were offered telephone repetitions , a seminar in risk intervention and , at the same seminar , further demonstration of the CDSS . The doctors received baseline registration s with information of how they treated their own hypertensive patients , and use of the CDSS was checked repeatedly . SETTING General practice in Sør- and Nord-Trøndelag counties in Norway , 380,000 inhabitants . PARTICIPANTS Seventeen health centres with 24 doctors and 984 patients in the intervention group . Data from 879 patients used in the final analyses . Twelve health centres with 29 doctors and 1255 patients in the control group . Data from 1119 patients used in the final analyses . MAIN OUTCOME MEASURES After an intervention period of 18 months , group differences in level of systolic and diastolic blood pressure , serum cholesterol , body mass index , and risk score for myocardial infa rct ion were calculated , as well as group differences in fractions of smokers . RESULTS Significant group difference in favour of intervention group : diastolic blood pressure 1 mmHg ( 95 % CI -1.89 , -0.17 ) . However , a significant baseline difference in systolic blood pressure in favour of control group of 2.7 mmHg ( 95 % CI 1.0 , 4.5 ) had been reduced to 1.2 mmHg ( 95 % CI -0.6 , 3.0 ) after intervention . CONCLUSION Implementation of clinical guidelines in the treatment of hypertensive patients in general practice by means of a CDSS and several other procedures for implementation did not affect patient outcome in any clinical ly significant way The objective was to evaluate the effect of an assessment strategy using the computer decision support system ( the GRAIDS software ) , on the management of familial cancer risk in British general practice in comparison with best current practice . The design included cluster r and omised controlled trial , and involved forty-five general practice teams in East Anglia , UK . R and omised to GRAIDS ( Genetic Risk Assessment on the Internet with Decision Support ) support ( intervention n=23 ) or comparison ( n=22 ) . Training in the new assessment strategy and access to the GRAIDS software ( GRAIDS arm ) was conducted , compared with an educational session and guidelines about managing familial breast and colorectal cancer risk ( comparison ) were mailed . Outcomes were measured at practice , practitioner and patient levels . The primary outcome measure , at practice level , was the proportion of referrals made to the Regional Genetics Clinic for familial breast or colorectal cancer that were consistent with referral guidelines . Other measures included practitioner confidence in managing familial cancer ( GRAIDS arm only ) and , in patients : cancer worry , risk perception and knowledge about familial cancer . There were more referrals to the Regional Genetics Clinic from GRAIDS than comparison practice s ( mean 6.2 and 3.2 referrals per 10 000 registered patients per year ; mean difference 3.0 referrals ; 95 % confidence interval ( CI ) 1.2–4.8 ; P=0.001 ) ; referrals from GRAIDS practice s were more likely to be consistent with referral guidelines ( odds ratio (OR)=5.2 ; 95 % CI 1.7–15.8 , P=0.006 ) . Patients referred from GRAIDS practice s had lower cancer worry scores at the point of referral ( mean difference −1.44 95 % CI −2.64 to −0.23 , P=0.02 ) . There were no differences in patient knowledge about familial cancer . The intervention increased GPs ' confidence in managing familial cancer . Compared with education and mailed guidelines , assessment including computer decision support increased the number and quality of referrals to the Regional Genetics Clinic for familial cancer risk , improved practitioner confidence and had no adverse psychological effects in patients . Trials are registered under N0181144343 in the UK National Research Register Abstract OBJECTIVE : Assess impact of a computer-based patient support system on quality of life in younger women with breast cancer , with particular emphasis on assisting the underserved . DESIGN : R and omized controlled trial conducted between 1995 and 1998 . SETTING : Five sites : two teaching hospitals ( Madison , Wis , and Chicago , Ill ) , two nonteaching hospitals ( Chicago , Ill ) , and a cancer re source center ( Indianapolis , Ind ) . The latter three sites treat many underserved patients . PARTICIPANTS : Newly diagnosed breast cancer patients ( N=246 ) under age 60 . INTERVENTIONS : Experimental group received Comprehensive Health Enhancement Support System ( CHESS ) , a home-based computer system providing information , decision-making , and emotional support . MEASUREMENTS AND MAIN RESULTS : Pretest and two posttest surveys ( at two- and five-month follow-up ) measured aspects of participation in care , social/information support , and quality of life . At two-month follow-up , the CHESS group was significantly more competent at seeking information , more comfortable participating in care , and had greater confidence in doctor(s ) . At five-month follow-up , the CHESS group had significantly better social support and also greater information competence . In addition , experimental assignment interacted with several indicators of medical underservice ( race , education , and lack of insurance ) , such that CHESS benefits were greater for the disadvantaged than the advantaged group . CONCLUSIONS : Computer-based patient support systems such as CHESS may benefit patients by providing information and social support , and increasing their participation in health care . These benefits may be largest for currently underserved population OBJECTIVES Prescribing alerts generated by computerized drug decision support ( CDDS ) may prevent drug-related morbidity . However , the vast majority of alerts are ignored because of clinical ir relevance . The ability to customize commercial alert systems should improve physician acceptance because the physician can select the circumstances and types of drug alerts that are viewed . We tested the effectiveness of two approaches to medication alert customization to reduce prevalence of prescribing problems : on-physician-dem and versus computer-triggered decision support . Physicians in each study condition were able to preset levels that triggered alerts . DESIGN This was a cluster trial with 28 primary care physicians r and omized to either automated or on-dem and CDDS in the MOXXI drug management system for 3,449 of their patients seen over the next 6 months . MEASUREMENTS The CDDS generated alerts for prescribing problems that could be customized by severity level . Prescribing problems included dosing errors , drug-drug , age , allergy , and disease interactions . Physicians r and omized to on-dem and activated the drug review when they considered it clinical ly relevant , whereas physicians r and omized to computer-triggered decision support viewed all alerts for electronic prescriptions in accordance with the severity level they selected for both prevalent and incident problems . Data from administrative cl aims and MOXXI were used to measure the difference in the prevalence of prescribing problems at the end of follow-up . RESULTS During follow-up , 50 % of the physicians receiving computer-triggered alerts modified the alert threshold ( n = 7 ) , and 21 % of the physicians in the alert-on-dem and group modified the alert level ( n = 3 ) . In the on-dem and group 4,445 prescribing problems were identified , 41 ( 0.9 % ) were seen by requested drug review , and in 31 problems ( 75.6 % ) the prescription was revised . In comparison , 668 ( 10.3 % ) of the 6,505 prescribing problems in the computer-triggered group were seen , and 81 ( 12.1 % ) were revised . The majority of alerts were ignored because the benefit was judged greater than the risk , the interaction was known , or the interaction was considered clinical ly not important ( computer-triggered : 75.8 % of 585 ignored alerts ; on-dem and : 90 % of 10 ignored alerts ) . At the end of follow-up , there was a significant reduction in therapeutic duplication problems in the computer-triggered group ( odds ratio 0.55 ; p = 0.02 ) but no difference in the overall prevalence of prescribing problems . CONCLUSION Customization of computer-triggered alert systems is more useful in detecting and resolving prescribing problems than on-dem and review , but neither approach was effective in reducing prescribing problems . New strategies are needed to maximize the use of drug decision support systems to reduce drug-related morbidity BACKGROUND Testing for pulmonary embolism often differs from that recommended by evidence -based guidelines . OBJECTIVE To assess the effectiveness of a h and held clinical decision-support system to improve the diagnostic work-up of suspected pulmonary embolism among patients in the emergency department . DESIGN Cluster r and omized trial . Assignment was by r and om-number table , providers were not blinded , and outcome assessment was automated . ( Clinical Trials.gov registration number : NCT00188032 ) . SETTING 20 emergency departments in France . PATIENTS 1103 and 1768 consecutive out patients with suspected pulmonary embolism . INTERVENTION After a preintervention period involving 20 centers and 1103 patients , in which providers grew accustomed to inputting clinical data into h and held devices and investigators assessed baseline testing , emergency departments were r and omly assigned to activation of a decision-support system on the devices ( 10 centers , 753 patients ) or posters and pocket cards that showed vali date d diagnostic strategies ( 10 centers , 1015 patients ) . MEASUREMENTS Appropriateness of diagnostic work-up , defined as any sequence of tests that yielded a posttest probability less than 5 % or greater than 85 % ( primary outcome ) or as strict adherence to guideline recommendations ( secondary outcome ) ; number of tests per patient ( secondary outcome ) . RESULTS The proportion of patients who received appropriate diagnostic work-ups was greater during the trial than in the preintervention period in both groups , but the increase was greater in the computer-based guidelines group ( adjusted mean difference in increase , 19.3 percentage points favoring computer-based guidelines [ 95 % CI , 2.9 to 35.6 percentage points ] ; P = 0.023 ) . Among patients with appropriate work-ups , those in the computer-based guidelines group received slightly fewer tests than did patients in the paper guidelines group ( mean tests per patient , 1.76 [ SD , 0.98 ] vs. 2.25 [ SD , 1.04 ] ; P < 0.001 ) . LIMITATION The study was not design ed to show a difference in the clinical outcomes of patients during follow-up . CONCLUSION A h and held decision-support system improved diagnostic decision making for patients with suspected pulmonary embolism in the emergency department A decision support system for the management of  oral hypoglycaemic therapy  in type II diabetes was evaluated . The ruleset contained therein forms the basis of a prototype computer programme , but in order to assess the robustness of the individual rules , it was decided it was necessary to use a paper-based form of the ruleset . A nurse with no previous experience of managing type II diabetes was trained to use the system and then undertook the exclusive management of half of all new type II diabetics , from a district population of 300,000 , over a 16-month period . General practice s within this area were divided into two groups , study and control , matching for size , geographical area and st and ards of existing diabetes care . Patients ( n = 102 ) from the study group practice s were then assigned to her care . Those patients ( n = 116 ) in the control group of practice s were treated according to their normal procedures . The decision support system for oral hypoglycaemic therapy was based on the following criteria : the current type of treatment ( six levels ) ; current glycaemic control ( HbA1 and FBS)-whether improving , steady or worsening ; and weight-%IBW , whether rising , steady or falling . Each of these parameters was carefully defined on the basis of established practice and clinical experience . Patients after initial education were seen at their usual clinic by the nurse only , on a monthly basis , until satisfactory glycaemic control was established and thereafter review ed 3 monthly . She was also responsible for ensuring the organisation of Diabetes Annual Review procedures . The medical records of the control group patients were examined at the end of the study and data on glycaemic control and Annual Review s extracted . In the study group 98 % patients achieved HbA1 levels within the normal range and all patients had full annual review s performed . The control practice s achieved much poorer degrees of metabolic control ( P < 0.01 ) and completed fewer annual review s. The study group did not demonstrate a significantly increased frequency of clinical hypoglycaemia consequent upon better blood sugar control . No exceptions to the ruleset , as initially defined , were detected . In conclusion , this decision support system was successful at achieving st and ards of diabetes control and care equal to or better than conventional structures of diabetes care . Implementation of such a system , on a simple computer platform , could greatly assist and possibly improve diabetes management in general practice Background Computer-based decision support has been effective in providing alerts for preventive care . Our objective was to determine whether a personalized asthma management computer-based decision support increases the quality of asthma management and reduces the rate of out-of-control episodes . Methods A cluster-r and omized trial was conducted in Quebec , Canada among 81 primary care physicians and 4447 of their asthmatic patients . Patients were followed from the first visit for 3–33 months . The physician control group used the Medical Office of the 21st century ( MOXXI ) system , an integrated electronic health record . A custom-developed asthma decision support system was integrated within MOXXI and was activated for physicians in the intervention group . Results At the first visit , 9.8 % ( intervention ) to 12.9 % ( control ) of patients had out-of-control asthma , which was defined as a patient having had an emergency room visit or hospitalization for respiratory-related problems and /or more than 250 doses of fast-acting β-agonist ( FABA ) dispensed in the past 3 months . By the end of the trial , there was a significant increase in the ratio of doses of inhaled corticosteroid use to fast-acting β-agonist ( 0.93 vs. 0.69 : difference : 0.27 ; 95 % CI : 0.02–0.51 ; P = 0.03 ) in the intervention group . The overall out-of-control asthma rate was 54.7 ( control ) and 46.2 ( intervention ) per 100 patients per year ( 100 PY ) , a non-significant rate difference of −8.7 ( 95 % CI : −24.7 , 7.3 ; P = 0.29 ) . The intervention ’s effect was greater for patients with out-of-control asthma at the beginning of the study , a group who accounted for 44.7 % of the 5597 out-of-control asthma events during follow-up , as there was a reduction in the event rate of −28.4 per 100 PY ( 95 % CI : −55.6 , −1.2 ; P = 0.04 ) compared to patients with in-control asthma at the beginning of the study ( −0.08 [ 95 % CI : −10.3 , 8.6 ; P = 0.86 ] ) . Discussion This study evaluated the effectiveness of a novel computer-assisted ADS system that facilitates systematic monitoring of asthma control status , follow-up of patients with out of control asthma , and evidence -based , patient-specific treatment recommendations . We found that physicians were more likely to use ADS for out-of-control patients , that in the majority of these patients , they were advised to add an inhaled corticosteroid or a leukotriene inhibitor to the patient s treatment regimen , and the intervention significantly increased the mean ratio of inhaled corticosteroids to FABA during follow-up . It also reduced the rate of out-of-control episodes during follow up among patients whose asthma was out-of-control at the time of study entry . Future research should assess whether coupling patient-specific treatment recommendations , automated follow-up , and home care with comparative feedback on quality and outcomes of care can improve guideline adoption and care outcomes . Conclusions A primary care-personalized asthma management system reduced the rate of out-of-control asthma episodes among patients whose asthma was poorly controlled at the study ’s onset . Trial Registration Clinical trials.gov Identifier : NCT00170248 http:// clinical PRIMARY OBJECTIVE To investigate whether computer decision support software used in the management of patients with asthma improves clinical outcomes . RESEARCH DESIGN R and omized controlled trial with practice s each reporting on 30 patients with asthma over a 6 month period . METHODS AND PROCEDURES 447 patients were r and omly selected from practice asthma registers managed by 17 general practice s from throughout the UK . Intervention practice s used the software during consultations with these patients throughout the study while control practice s did not . MAIN OUTCOMES AND RESULTS Practice consultations , acute exacerbations of asthma , hospital contacts , symptoms on assessment and medication use . A smaller proportion of patients within the intervention group initiated practice consultations for their asthma : 34 ( 22 % ) vs 111 ( 34 % ) , odds ratio ( OR ) = 0.59 , 95 % confidence interval ( CI ) ( 0.37 - 0.95 ) ; and suffered acute asthma exacerbations : 12 ( 8 % ) vs 57 ( 17 % ) , OR = 0.43 , 95 % CI = 0.21 - 0.85 six months after the introduction of the computer decision support software . There were no discernable differences in reported symptoms , maintenance prescribing or use of hospital services between the two groups . CONCLUSION The use of computer decision support software that implements guidelines during patient consultations may improve clinical outcomes for patients with asthma BACKGROUND AND OBJECTIVES We carried out a prospect i ve , r and omized trial to test whether a computer-based decision support system to initiate and maintain oral anticoagulant ( OA ) treatment can improve the laboratory quality of therapy . DESIGN AND METHODS Two separate sets of patients on oral anticoagulants , in five Italian anticoagulant clinics , were studied : 335 patients in the first three months of treatment ( stabilization phase ) , 916 patients ( 775 patient-years ) beyond the third month of treatment ( maintenance phase ) . Patients were r and omized to a computerized system , which included algorithms able to suggest OA dosing and to schedule appointments ( computer-aided dosing ) or to an arm in which OA were prescribed by the same teams of expert physicians without such algorithms ( control group ) . Primary outcomes were : A ) the percentage of patients reaching a stable state of anticoagulation during each of the first three months of treatment ; B ) the percentage of time individuals spent within the aim ed therapeutic range ( maintenance phase ) . RESULTS Patients in the computer-aided dosing group achieved a stable state significantly faster ( p<0.01 ) and they spent more time within the therapeutic range during maintenance ( p<0.001 ) than controls . The favorable effect of computer-aided dosing was mainly due to a reduction of the time spent below the therapeutic range and was associated with an increase of mean INR value , of anticoagulant drug dosage , and with a reduction of the number of appointments per patient ( all changes significant : p<0.001 ) . INTERPRETATION AND CONCLUSIONS The computer decision-aided support improves the laboratory quality of anticoagulant treatment , both during long-term maintenance and in the early , highly unstable phase of treatment , and it also significantly reduces the number of scheduled laboratory controls BACKGROUND Identifying the appropriate long-term anti-thrombotic therapy following acute ischaemic stroke is a challenging area in which computer-based decision support may provide assistance . AIM To evaluate the influence on prescribing practice of a computer-based decision support system ( CDSS ) that provided patient-specific estimates of the expected ischaemic and haemorrhagic vascular event rates under each potential anti-thrombotic therapy . DESIGN Cluster-r and omized controlled trial . METHODS We recruited patients who presented for a first investigation of ischaemic stroke or TIA symptoms , excluding those with a poor prognosis or major contraindication to anticoagulation . After observation of routine prescribing practice ( 6 months ) in each hospital , centres were r and omized for 6 months to either control ( routine practice observed ) or intervention ( practice observed while the CDSS provided patient-specific information ) . We compared , between control and intervention centres , the risk reduction ( estimated by the CDSS ) in ischaemic and haemorrhagic vascular events achieved by long-term anti-thrombotic therapy , and the proportions of subjects prescribed the optimal therapy identified by the CDSS . RESULTS Sixteen hospitals recruited 1952 subjects . When the CDSS provided information , the mean relative risk reduction attained by prescribing increased by 2.7 percentage units ( 95%CI -0.3 to 5.7 ) and the odds ratio for the optimal therapy being prescribed was 1.32 ( 0.83 to 1.80 ) . Some 55 % ( 5/9 ) of clinicians believed the CDSS had influenced their prescribing . CONCLUSIONS Cluster-r and omized trials provide excellent frameworks for evaluating novel clinical management methods . Our CDSS was feasible to implement and acceptable to clinicians , but did not substantially influence prescribing practice for anti-thrombotic drugs after acute ischaemic stroke Caregivers of persons with Alzheimer 's disease ( AD ) are confronted with novel decision-making tasks that leave them at risk for social isolation . Computer Link is a computer network providing information , communication , and decision-support functions for caregivers of persons with AD . One hundred two AD caregivers participated in a r and omized field experiment to evaluate the effects of ComputerLink on confidence in decision making , decision-making skill , and social isolation . During the 1-year study period , AD caregivers averaged two encounters per week with Computer Link for sessions lasting an average of 13 minutes . Access to Computer Link enhanced caregivers'decision-making confidence . Decision-making skill was unaffected . ComputerLink access did not lead to changes in social isolation as measured by st and ard instruments . The decision-support function was used least often . Postings to ComputerLink 's public bulletin board included both information-seeking and supportive messages . These findings are congruent with reports of other community-based interventions for caregivers and consistent with evaluations of computerized decision-support systems OBJECTIVE This study assessed the effect of a h and held computer-based decision support system ( DSS ) on  antibiotic use and patient outcomes in a critical care unit . DESIGN A DSS containing four types of evidence ( patient microbiology reports , local antibiotic guidelines , unit-specific antibiotic susceptibility data for common bacterial pathogens , and a clinical pulmonary infection score calculator ) was developed and implemented on a  h and held computer  for use in the intensive care unit at a tertiary referral hospital . System impact was assessed in a prospect i ve " before/after " cohort trial lasting 12 months . Outcome measures were defined daily doses ( DDDs ) of antibiotics per 1,000 patient-days , patient length of stay , and mortality . RESULTS The number of admissions , APACHE ( Acute Physiology , Age , and Chronic Health Evaluation ) II and SAPS ( Simplified Acute Physiology Score ) II for patients in preintervention , and intervention ( DSS use ) periods were statistically comparable . The mean patient length of stay and the use of antibiotics in the unit during six months of the DSS use decreased from 7.15 to 6.22 bed-days ( p = 0.02 ) and from 1,767 DDD to 1,458 DDD per 1,000 patient-days ( p = 0.04 ) , respectively , with no change in mortality . The DSS was accessed 674 times during 168 days of the trial . Microbiology reports and antibiotic guidelines were the two most commonly used ( 53 % and 22.5 % , respectively ) types of evidence . The greatest reduction was observed in the use of beta-lactamase-resistant penicillins and vancomycin . CONCLUSION H and held computer-based decision support contributed to a significant reduction in patient length of stay and antibiotic prescribing in a critical care unit Abstract Objectives : To evaluate the potential effect of computer support on general practitioners ' management of familial breast and ovarian cancer  , and to compare the effectiveness of two different types of computer program . Design : Crossover experiment with balanced block design . Participants : Of a r and om sample of 100 general practitioners from Buckinghamshire who were invited , 41 agreed to participate . From these , 36 were selected for a fully balanced study . Interventions : Doctors managed 18 simulated cases : 6 with computerised decision support system Risk Assessment in Genetics ( RAGs ) , 6 with Cyrillic ( an established pedigree drawing program design ed for clinical geneticists ) , and 6 with pen and paper . Main outcome measures : Number of appropriate management decisions made ( maximum 6 ) , mean time taken to reach a decision , number of pedigrees accurately drawn ( maximum 6 ) . Secondary measures were method of support preferred for particular aspects of managing family histories of cancer ; importance of specific information on cancer genetics that might be provided by an “ ideal computer program . ” Results : RAGs result ed in significantly more appropriate management decisions ( median 6 ) than either Cyrillic ( median 3 ) or pen and paper ( median 3 ) ; median difference between RAGs and Cyrillic 2.5 ( 95 % confidence interval 2.0 to 3.0 ; P<0.0001 ) . RAGs also result ed in significantly more accurate pedigrees ( median 5 ) than both Cyrillic ( median 3.5 ) and pen and paper ( median 2 ) ; median difference between RAGs and Cyrillic 1.5 ( 1.0 to 2.0 ; P<0.0001 ) . The time taken to use RAGs  ( median 178 seconds ) was 51 seconds longer per case ( 95 % confidence interval 36 to 65 ; P<0.0001 ) than pen and paper ( median 124 seconds ) but was less than Cyrillic ( median 203 seconds ; difference 23 . ( 5 to 43 ; P=0.02 ) ) . 33 doctors ( 92 % ( 78 % to 98 % ) ) preferred using RAGs overall . The most important elements of an “ ideal computer program ” for genetic advice in primary care were referral advice , the capacity to create pedigrees , and provision of evidence and explanations to support advice . Conclusions : RAGs could enable general practitioners to be more effective gatekeepers to genetics services , empowering them to reassure the majority of patients with a family history of breast and ovarian cancer who are not at increased genetic risk
: It seems that computers have a favourable effect on the uptake and follow up of patients in hypertension management .
STUDY OBJECTIVE : To assess the effect of computers and computer-based clinical decision support systems on the  management of hypertension .