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
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
: 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 .