CATCH-IT Draft: Effect of guideline based computerised decision support on decision making of multidisciplinary teams

CATCH-IT Draft: Effect of guideline based computerised decision support on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation

Goud R, de Keizer NF, ter Riet G, Wyatt JC, Hasman A, Hellemans IM, Peek N. Effect of guideline based computerised decision support on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation. BMJ. 2009;338:b1440.

Abstract / Full Text / Slideshow

Introduction

In this article in BMJ, the authors investigated the effect of computerized decision support (CDS) on guideline adherence to recommended therapeutic decisions in multidisciplinary teams. They investigated the use of the Cardiac Rehabilitation Decision Support System (CARDSS) in promoting adherence to the Dutch Cardiac Rehabilitation Guidelines in cardiac rehabilitation centres in the Netherlands. It is the first study to my knowledge to evaluate the effect of CDS on decision making in teams. The article would be of interest to health care settings with multidisciplinary teams who are thinking about adding CDS to their center.

Discussion

The study design was a randomized clustered trial. In order to investigate the effect of CARDSS on the care providers, the trial had to randomize by team/center rather than randomize on the patient level since the study was essentially testing the care provider teams. Therefore, it would not be feasible to include both the intervention and control group in the same center since the teams can learn from the intervention. Having a randomized clustered design may subtract from the ability to test a true difference, however, due to the nature of the trial it was a necessity.

There was some concern for the high attrition rate in the study. Initially, there were 31 randomized centers, but 5 centres discontinued participation and another 5 were excluded. Although the authors stated that the centres were excluded based on data discrepancies or missing data, while also being the lead developers of the CARDSS system, they may have been influenced on which centres to exclude which might have shown the system as having a negative effect on guideline adherence. However, the authors also took steps to reduce this bias such as blinding the investigators during randomization allocation, use of objective outcome measures, and involvement of an external evaluator and statistician.

With this high attrition rate, there was a possible issue with the study having insufficient statistical power as the authors stated that they required 36 participating centers during their six month follow up to detect a 10% absolute difference in adherence rate with 80% power at a type I error risk (a) of 5%. However, in the end they were only able to include 21 centres in their analysis. Although, the 21 centers may have provided a large enough sample size by having a larger number of patients per a center, their analysis still resulted in wide confidence intervals such as the borderline significance found for exercise therapy (95% CI 0% to 5.4%). In addition, the authors did not explain how they calculated the adjusted difference or confidence intervals. It was not clear that the Improvement seen in the intervention group in comparison with control group was not influenced by the confounding factors. There was also no explanation on how the covariates (age, sex, diagnosis, weekly volume of new patients, whether the center is a specialized rehabilitation center or part of academic hospital) influenced the rate of adherence.

To account for the initial learning curve to the CARDSS, the authors provided a standardized training course for the care providers and excluded the data of patients enrolled in the first two weeks of CARDSS being used in each participating centre from the analyses. The authors stated that CARDSS was judged favourably in a usability study. However, the fact that three centres were excluded from the study for not recording their decisions on CARDSS and two centers for too much missing data may indicate a possible usability issue. It may be possible to re-engineer the software in a way to reduce missing recorded decisions and data. The data audit which the authors used to exclude the centres by comparing the record keeping on CARDSS and the paper based patient record was one of the strengths of the study. If there were discrepancies in the data between the two records, the authors considered the centre in question to be unreliable and excluded them from the analyses. If a centre passed the data audit but data analysis indicated that 20% or more of a centre’s patients’ records had missing data, the authors also excluded that centre from the analyses. The authors did perform a follow up qualitative study performed afterwards which looked into how CARDSS affected the main barriers to the implementation of guidelines for cardiac rehabilitation. In this follow up study, there was more qualitative user feedback from the interviews on the usability of CARDSS.

Although the authors stated that the study design ensured that there would be no bias from the Hawthorne effect, they did not go into on detail how this was accomplished. There could have been a possible Hawthorne effect since the care providers were aware they were in a study with their performance being monitored and reported which may have prompted them to increase their adherence to guidelines. There was also the potential bias resulting from having the care providers record their reason for cases where they do not adhere to the guidelines. They may simply go along with the recommended therapy from CARDSS since it would prevent additional work.

It would have been also nice if the investigators collected baseline adherence data prior to implementing the CARDSS at the centres. This would allow comparisons to be made between pre-implementation and post-implementation of CARDSS. The control group in the current study design is unable to do this since they were still given a CARDSS. Even if their version had limited functionality, it would have an effect on the results. It would also have been interesting to investigate guideline adherence rates in the control group after they have been given full functionality of the CARDSS in a follow up trial. The authors do have an ongoing follow up trial which may be looking into this aspect.

Finally, there was the issue regarding the lack of ethics approval for the study. It was stated in the article that ethics approval was not needed according to the medical ethics committee of the Academic Medical Centre in Amsterdam.

Conclusion

The authors concluded that CARDSS improved adherence to guideline recommendations with respect to three out of the four therapies: exercise, education, and relaxation therapy. There was no effect on lifestyle change therapy which may be partly due to the fact that the majority of clinics did not have the therapy program available. Although there are some weaknesses and limitations in this study, the authors hope to address most of them in their follow up randomized cluster study.

Questions to the authors

• Why was baseline adherence data not collected?
• How do the covariates affect guideline adherence?
• How were the adjusted differences and CI values calculated?
• What is the reason for large variation in adherence between centers?
• Why was ethics approval not needed?
• Why was CARDSS not designed to be interoperable with other information systems?
• How did the authors account for the effect of the control group knowing they were in the control group?

Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems

WRIGHT A, SITTIG DF, ASH JS, SHARMA S, PANG JE, MIDDLETON B. Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems. J Am Med Inform Assoc. 2009; 16(5): 637-644

Full Text: click here

A b s t r a c t

Background: The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems.

Purpose: The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems.

Methods: The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features.

Results: Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user’s workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert.
Discussion: The principal finding relates to system-by-system variability. The best system in our analysis had onlya single missing feature (from 42 total) while the worst had eighteen. This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern.

Conclusions: These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

The unintended consequences of computerized provider order entry: findings from a mixed methods exploration.

Ash JS, Sittig DF, Dykstra R, Campbell E, Guappone K. The unintended consequences of computerized provider order entry: Findings from a mixed methods exploration. Int J Med Inf. 2009;78(SUPPL. 1):69-76

Full Text

(Please note that after reading this paper you may want to also look at the following papers to gather a better understanding of the results from their research:

1. Ash, J.S., Sittig, D.F., Poon, E.G., Guappone, K., Campbell, E., Dykstra, R.H.The Extent and Importance of Unintended Consequences Related to Computerized Provider Order Entry(2007) Journal of the American Medical Informatics Association, 14 (4), pp. 415-423

2. Campbell, E.M., Sittig, D.F., Ash, J.S., Guappone, K.P., Dykstra, R.H.Types of Unintended Consequences Related to Computerized Provider Order Entry(2006) Journal of the American Medical Informatics Association, 13 (5), pp. 547-556.)

OBJECTIVE

To describe the foci, activities, methods, and results of a 4-year research project identifying the unintended consequences of computerized provider order entry (CPOE).

METHODS

Using a mixed methods approach, we identified and categorized into nine types 380 examples of the unintended consequences of CPOE gleaned from fieldwork data and a conference of experts. We then conducted a national survey in the U.S.A. to discover how hospitals with varying levels of infusion, a measure of CPOE sophistication, recognize and deal with unintended consequences. The research team, with assistance from experts, identified strategies for managing the nine types of unintended adverse consequences and developed and disseminated tools for CPOE implementers to help in addressing these consequences.

RESULTS

Hospitals reported that levels of infusion are quite high and that these types of unintended consequences are common. Strategies for avoiding or managing the unintended consequences are similar to best practices for CPOE success published in the literature.

CONCLUSION

Development of a taxonomy of types of unintended adverse consequences of CPOE using qualitative methods allowed us to craft a national survey and discover how widespread these consequences are. Using mixed methods, we were able to structure an approach for addressing the skillful management of unintended consequences as well.

CATCH-IT DRAFT: Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial.

Holbrook, A., Thebane, L., Keshavjee, K., Dolovich, L., Bernstein, B., Chan, D., Troyan, S., Foster, G., Gerstein, H. Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. CMAJ. 2009 Jul 7;181(1-2):37-44

Links: Abstract - Full Text - Slideshow - Final Paper

Introduction

Diabetes Mellitus is a complex condition. The inherent nature of the disease makes it an ideal target to care for using eHealth innovations. However, research on electronic applications that address chronic diseases is limited; the usefulness of such applications has not been thoroughly investigated. From 2000-2003, Health Canada funded a project titled ‘Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness’ (COMPETE). The second phase of the project (COMPETE II) evaluated the use of a “Web-based, continuously updated, patient-specific diabetes tracker available to patient and physician plus an automated telephone reminder service for patients, on access, quality, satisfaction and continuity of care” (1). The results of the project have been published by Holbrook et al (2) and are discussed herein.

Objectives of the study

The authors state that the rationale for the study is because “there have been few randomized trials to confirm that computerized decision support systems can reliably improve patient outcomes” (2). Thus, this study is to add to the literature. They hypothesize that patients within the intervention group – those with access to the decision support tool and receiving telephone reminders – would have improved quality of diabetes care. Interestingly, the deliberate goal of the COMPETE II project, within which the study was conducted, was to “have patients regularly visit their family physician” (1). These goals, although similar in nature, are explicitly different. This difference in goals brings into question if the study was conducted proactively or retrospectively.

Methodological issues

In selecting patients for the intervention group, randomization occurred using allocation concealment through central computer generation of group assignment, which was subsequently stratified by the provider in groups of 6. Although the authors attempt to reduce bias into the sample, randomization at the patient level could have caused contamination due to possible interactions amongst patient of the same physician.

One major flaw in the study design was that patients within the intervention group were initially required to set up an appointment with their family physician in addition to getting relevant lab tests done. Patients within the control group did not. This is of particular interest because the outcome measure for the intervention (the process composite score) was based on the frequency of patient visits and lab tests. Therefore, one cannot determine whether or not the change in the outcome measure was due to the intervention or the initial physician visit and lab tests.

Another issue that requires discussion is the timeframe of the study. Patients were only tracked for 6 months even though the study was conducted over the time span of a year. Tracking over a longer time period would have made the process score more valuable insofar as many of the process targets were semiannual. Thus, these targets would have already been met just by the primary physician visit required of intervention participants.

Discussion

The authors interpret the results of there study as being that they have “demonstrated that the care of complex chronic disease can be improved with electronic tracking and decision support shared by providers and patients” (2). This statement seems to go beyond the evidence of the study. For example, the inherent flaws of the intervention and the outcome measures brought the authors’ conclusion into question.

The intervention

The intervention is described as a web-based diabetes tracker that includes decision support. The electronic tracker was built to integrate with the care providers electronic medical records (EMRs) as well as with an automated telephone reminder system. Patients also received mail out tracking reports. The multi-nodal nature of the intervention makes it difficult to determine which component is the causative agent for the change observed. The authors do not comment on the utilization of the tracker nor the nature of the telephone reminders. In addition, 51.4% of patients in the intervention group ‘never’ used the internet. How then, is it possible for the authors to imply that the causative agent of change is the electronic decision support if more than half of the sample does not use the tool? Utilization values for the decision support tool would substantiate this claim with quantitative evidence.

Outcome measures

One strength of the study is in how the authors determined the variables to measure. These were based on the guidelines from the Canadian and American Diabetes Associations, literature reviews, and expert opinions. As a result, they have determined two outcome measures based on the frequency of patient-clinician interactions (process score), and the improvement in clinical outcomes (clinical score) based on best practices. However, the authors do not validate their choice in variable weight. Take, for example, the process score. Two variables, weight and physical activity, both having quarterly process targets are weighted differently - weight and physical activity having maximum score of 2 and 1 respectively. Furthermore, individuals in the intervention group started the study with a physician and lab visit. Thus their process score would be high irrespective of the intervention. Why wasn’t the control group also sent in for physician and lab visits?

The presentation of the clinical target scores is questionable. Only two variables showed statistically significant improvement. However, there were two variables that did not improve - exercise and smoking. Both scores had a value of 1.00 before the intervention and 0.69 after the intervention. These results appear counterintuitive. Interestingly the authors attribute the positive change to the intervention tool, but do not discuss the reasons for the decrease in score.

Conclusion

The authors have not validated their interpretation sufficiently. The multi-nodal nature of the intervention makes it difficult to correlate intervention with outcomes. In addition, the study does not indicate which component is the causative agent. Although this study adds to the growing body of literature on eHealth tools and their effect on chronic disease management, the rigor applied in the methodology is not strong enough to substantiate the authors’ claims. Therefore, it is difficult to support the validity and relevance of the study’s conclusion.

Acknowledgement

Thank you to the 2009 CATCH-IT Journal Club members from the University of Toronto’s HAD 5726 course along with Professor Eysenbach for their feedback and critical analysis that has greatly contributed to this report.

References

(1) COMPETE II. Available at: http://www.hc-sc.gc.ca/hcs-sss/pubs/chipp-ppics/2003-compete/final-eng.php. Accessed Oct 15, 2009.

(2) Holbrook A, Thabane L, Keshavjee K, Dolovich L, Bernstein B, Chan D, et al. Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. CMAJ 2009 Jul 7;181(1-2):37-44.

CATCH-IT Final Report: Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension

Links: Abstract . Comments . Draft Report . Presentation

Park MJ, Kim HS, Kim KS. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. International journal of medical informatics. 2009 Oct;78(10):704-10.

Introduction

Globally overweight and obesity, representing at least 300 million clinically obese persons, poses a major risk for chronic diseases, including type 2 diabetes, cardiovascular disease, hypertension and stroke ⁽¹⁾. Increasingly, health care planners are looking for affordable strategies ⁽²⁾ inclusive of leveraging the use of information and communication technology (ICT) as a viable tool to support patients’ self-management of these diseases. This is a critical appraisal review of one of the most recent studies, which evaluates the use of both cellular phone and internet as an intervention in the self-management of clinical measures for obese hypertension.

One of the authors of this paper, Hee-Sung Kim, has experience with previous research studies using ICT for chronic disease management dated back to 2003. No further work in this area is established for the other authors.

Objectives of Study

The aim of the study is to evaluate whether an intervention using short message service (SMS) by cellular phone and Internet would improve blood pressure (BP), weight control, and serum lipids of obese patients with hypertension during 8 weeks. The authors cite the rationale for this as “no study has been done to test the direct efficacy of the cellular phone or internet-based system” on improving of these measures for hypertension”. Logan etal, 2007 presented a paper on use of both inventions, which is referenced by authors making this not a novel research.

Methodological Issues

Firstly, the intervention is not clearly defined, “.. intervention group were requested to record their blood pressure and body weight in a weekly web based diary through the Internet or by cellular phones.” No justification is given for the choosing SMS or descriptive given of how the patient would input information, if any, from the cellular phone. No clear indication is stated as to how the input of information on BP, weight and drug information would allow the system to give advice about fast food intake and exercise duration. In addition, given that the data is self-reported, there is no indication of any objective way to confirm the data reported on which the decisions are made for the SMS alerts. In the research by Logan etal (2007), a Bluetooth-enabled home BP monitor is used for greater validity of information.

Secondly, omitted is the actual population size of the data collected from which the participants are drawn as well as how the sample is selected. Associated with this is the potential for selection bias, as it is unclear who selected the control group and what other factors may have been considered in addition to matching the age, sex, systolic BP, diastolic BP and body weight to the intervention group at the same department. Amongst other internal validities that are observed, this appears to pose the single most important threat, as it is possible to pull patient records with no change in the clinical outcome variables, unless this was a blinded process.

Thirdly, usage data of the intervention is omitted. Review done on related articles presenting studies with the use of ICT intervention such as by Patrick etal (2009), Raab etal (2009), Cocosila etal (2009), Morak etal (2008), Logan etal, (2007), and Kwon etal (2004), report results in addition to the clinical outcome. Data expected are those such as mean number of logon times per patient per day, alerts sent from both SMS and internet, entries for clinical measures such as blood pressure, weight, drug entries and most frequent comments over the period. In addition, how did the researchers analyze this data when not all patients had access to a computer of phone?

Fourthly, given that the study incorporates the behavioural pattern of patients, the theoretical approach used is not stated explicitly for self-efficacy ⁽⁴⁾. This would enrich the research for persons interested in cognitive and behavioural research.

Discussion

This study is contributing to the body of literature on behavioural change through online intervention, still a relatively new area of research and will prompt development of more in-depth research. However, the results must be taken with caution base on the fundamentally flawed methodological issues that is associated with the research.

Hee-Seung Kim has authored one publication ⁽⁵⁾ and co-authored eleven publications. In one of the earlier researches co-authored in 2004, “Establishment of Blood Glucose Monitoring System Using the Internet” ⁽⁶⁾, the ICT intervention is clearly defined with inclusion screenshots and reporting of the results as seen in related researches ^(7,8,9,10). It is quite noticeable that subsequent studies have used the same web-based diary intervention from the same institution, which has been extended into variations of research papers on diabetes management in conjunction with SMS cellular phone. This study has portions of the writing that are verbatim with blind application of parts of study with no evidence of results or relevance. How much of this study is original work and could this have lead to the decrease in the quality of the reporting and exclusion of the pertinent information alluded to in the methodological issues above? The authors state that some patients may not have had access to a computer or able to use a cell phone, despite the inclusion criterion that they "should be able to input data into the website and have their own cellular phone". Clearly, this is conflicting and poses additional difficulty on the validity of assessing “direct efficacy”.

Another critical element missing from this and past reports of similar study by the authors is the lack of information regarding the patients’ perspective on the ease of use, acceptance and effectiveness of the interventions. It would be valuable to know the extent to which patients find the ICT interventions to be helpful in disease self-management, increased self-efficacy, and treatment adherence, as the technology becomes an integral part of people's everyday life. This information would also help to inform future research and long-term planning.

Conclusion

The authors conclude, “the intervention using SMS of cellular phone and Internet improved blood pressure, body weight, waist circumference, and HDL-C at 8 weeks in obese hypertensive patients.” However, given the number of concerns regarding the methodological issues, limited timeline of this intervention, and lack of generalization due to low sample size; these will greatly limit the level of confidence in all inferences that might be drawn from this study deeming the results not valid. Overall, the poor quality of reporting has detracted from the goal of the study.

Questions to the Authors

1. What is the usage data of the cellular phone and internet intervention such as daily frequency response rate per patient per measure, number of alerts sent, and number of entries for clinical measures over the 8 weeks period?

2. How was the data analyzed to determine alerts to be sent if not all patients had access to a computer of phone?

3. What is the actual population size of the data collected from which the participants are drawn?

4. Who did the selection of the control group and how exactly was this done? What were the variables used for matching? What is the potential for a selection bias?

5. What is the rationale for exclusion of patients that changed medication during the period of the interventions and how many persons were excluded due to this in the intervention and control group?

6. What specific differences are identified using the paired t-test with Bonferroni correction and why is ANOVA used rather than t-test when comparing the groups?

7. What exactly was the paired t-test with Bonferroni correction used for?

8. Are the findings presented in the results of statistical significance only or were these also verified for clinical significance?

9. What measurement is used to determine self-efficacy in the adherence to control of hypertension?

10. Why is the patient’s perspective not included on the usability and effectiveness of the intervention?

11. Do you think doing a qualitative study of patients' perspectives might have altered the results or help to inform future research and long-term planning.

Acknowledgement

Thank you to the Professor Eysenbach and fellow graduate students of the 2009 CATCH-IT Journal Club at the University of Toronto, for their helpful and insightful discussion and comments that contributed to this report.

References

1. World Health Organization. Obesity and overweight. [Online].; 2003 [cited 2009 October Available from: http://www.who.int/dietphysicalactivity/publications/facts/obesity/en/.

2. Prentice A. The emerging epidemic of obesity in developing countries. Int. J. Epidemiol. 2006; 35: p. 93–99.

3. Park M, Kim H, Kim K. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. Int J Med Inform. 2009 Oct; 78(10): p. 704-10.

4. Kim H. A randomized controlled trial of a nurse short-message service by cellular phone for people with diabetes. Int J Nurs Stud. 2007 July; 44(5): p. 687-692.

5. Kwon H, Cho J, Kim H, Song B, Ko S, Lee J, et al. Establishment of Blood Glucose Monitoring System Using the Internet. Diabetes Care. 2004 February; 27(2): p. 478-483.

6. Logan AG, McIsaac WJ, Tisler A, Irvine MJ, Saunders A, Dunai A, et al. Mobile Phone-Based Remote Patient Monitoring System for Management of Hypertension in Diabetic Patients. Am J Hypertens. 2007 September; 20(9): p. 942-948.

7. Morak J, Schindler K, Goerzer E, Kastner P, Toplak H, Ludvik B, et al. A pilot study of mobile phone-based therapy for obese patients. J Telemed Telecare. 2008; 14(3): p. 147-9.

8. Cocosila M, Archer N, Haynes RB, Yuan Y. Can wireless text messaging improve adherence to preventive activities? Results of a randomised controlled trial. Int J Med Inform. 2009 April; 78(4): p. 230-238.

9. Patrick K, Raab F, Adams M, Dillon L, Zabinski M, Rock C, et al. A Text Message–Based Intervention for Weight Loss: Randomized Controlled Trial. J Med Internet Res. 2009; 11(1 ): e1.

10. Anhoj J, Moldrup C. Feasibility of Collecting Diary Data From Asthma Patients Through Mobile Phones and SMS (Short Message Service): Response Rate Analysis and Focus Group Evaluation From a Pilot Study. J Med Internet Res. 2004 Oct–Dec; 6(4): e42.

CATCH-IT Final Report: The Relationship between Electronic Health Record Use and Quality of Care over Time

Abstract and initial discussion

CATCH-IT Draft Report

Presentation on Slideshare

Zhou L, Soran CS, Jenter CA, Volk LA, Orav EJ, Bates DW, Simon, SR. Relationship between EHR use and Quality of Care over Time. J Am Med Inform Assoc. Jul-Aug 2009;16:457– 464.

Introduction

The paper chosen for this particular CATCH-IT report is entitled, The Relationship between Electronic Health Record Use and Quality of Care over Time. It was written by the research team of Zhou, Soran, Jenter, Volk, Orav, Bates and Simon. This team consisted of the leading researchers in electronic health records (EHR) from Columbia University and Harvard University. This paper can be considered a high quality paper because this group has conducted what little research there has been in this field. The results of this paper may have a potential impact on the decision of hospital administrators and clinicians to use EHRs in their respective practice settings. It was also picked because electronic health records are currently a very timely issue on the political agenda of many Western countries.

This paper would be interesting for a number of stakeholders. It would interest researchers because it suggests that further research is required. This would enable future work for researchers in the field of quality and EHRs. There are also the quality measurement criteria that could be improved. It may also appeal to researchers because there is a potential for other quality of care data sets to be used to compare against individual clinician EHR usage. It would be of interest to politicians and other policy makers, such as lobbyists, researchers, ministries of health, etc. It may be especially useful to the critics of eHealth because the conclusions drawn state that the use of EHR is not associated with improved healthcare outcomes. This can be used as an argument for government excess or spending. Other policy makers may be able to conclude that there is no inherent value in EHRs. This would affect the policies for the particular jurisdiction these policy makers reside. The media may also use the results of this study to show that there may not be any inherent value in having an EHR at all. However, since this paper only discusses the quality aspect of care, this may not be as much of a disincentive to stop the investment in EHRs. There are other aspects to an EHR which may be of more importance such as reduction of paper, improved process flows and greater physician communication, to name a few.

Discussion

Some of the background information drawn for this paper is the methodology for the clinician EHR usage survey. To obtain this (and the survey) one would have to check the reference list and contact the author of that particular study which only measured physician EHR usage. It was noted upon reading the reference paper by Simon et al. that there were many physicians who had moved, were deceased or were no longer practicing medicine who received the survey.

This article studied the use of EHR and the quality of care given by clinicians in various practices settings. The study employed the use of two data sets. It is of interest to note that both data sets used are secondary data and were not created for the purpose intended by the study authors. One survey measured physicians’ adoption and use of EHR in the state of Massachusetts. It used an eight page questionnaire with a stratified random sample, i.e., hospital-based vs. large vs. rural practices, by specialty, etc. Sample weights were used to adjust for overall representation. For example, if there was a total of 70 physicians, and only 50 responded, the authors would perform an adjustment calculation, i.e., 50/70 multiplied by each answer to represent the entire population. The second data set was statewide data on physicians’ quality of care as indicated by their performance on widely used quality measures. The secondary data set was drawn from a HEDIS database. A HEDIS database is populated using insurance claims data. There were certain exclusion criteria set for the HEDIS data which made respondents younger, more recently graduated, in smaller practices with more patient visits and more likely to be female. These two data sets were integrated and linked for each physician. The outcome measures used were core EHR functions and associated features (i.e., health information and data, result management, order entry and management, decision support, and electronic communication and connectivity) as well as the HEDIS quality measuses which were aggregated into six clinical categories (i.e., asthma care, behavioural and mental health, cancer screening, diabetes care, well child and adolescent visit, and women’s health). Also, the use of a feature such as decision support in this type of measure is misleading. The extent of decision support varies as well as the functionality as this feature has progressed from 2001 through 2005.

As mentioned, this study examined the cross-sectional relationship between having an EHR and concurrent indicators of quality of care. The authors carried out a longitudinal analysis to study the trend of EHR adoption and usage as well as to examine the association between the duration of EHR usage and quality of care. They compared the HEDIS quality measures of respondents to the length of time using an EHR. The results show that EHR adoption increased over time. For the purposes of this study, adoption was equated to the availability of an EHR in a particular practice. This may not be the case. There can be physician who have an EHR available yet choose not to use it. The results also showed that the availability and use of EHR core functions increased. The usage of specific features was measured and presented in the paper as used most of the time, some of the time or none of the time. There was no statistical difference between EHR users and non-users. Finally, and most importantly, usage of an EHR was not related to increased quality of care.

There were only two references cited for relationship of EHR use and quality of care. This would seem to indicate a need for more research to be conducted in this field. However, the difficulties with conducting this type of research have already been explained. The implications for policy makers would be that there may be a need to pay more for higher quality care. This may ensure that physicians are using EHRs to their full capacity. This can be achieved through education and workflow transformation. There may need to be stronger incentives or more extensive programs to support physician office transformation. One strong argument the authors suggest is that The 2005 EHR adoption rate will need to triple in less than a decade (for MA) for 100% physician EHR usage. This represents a challenge for health care policy.

Questions to the authors:

Would it be possible to use a different data set in order to measure quality of care? Do you believe that lack of effect on HEDIS data represents a lack of impact on quality?

Would/did you consider creating your own survey instrument for this study rather than secondary data?

Would the use of an insurance claim dataset automatically exclude the care of those without health insurance? If so, how would you account for this?

How were the adjusted calculations performed for the quality of care measures?

Is there any possible way the HEDIS data set could have been used for longitudinal analysis of EHR adoption/use and quality of care?

Why was the availability of a system equated to adoption whereas, usage was separately measured?

Conclusion

The results are of this paper are valid. This paper addressed the research question it proposed from the outset. The data used shows that there is no clear connection between EHR usage and quality of care. However, it should be noted that the results are shown with adjusted values. It should be recognized that it is difficult to conduct these types of studies. There are measurement issues, one cannot prove correlation can be equated to causation and there are adjustments for variables. Upon initial implementation, health care quality cannot be positively associated with the systems which are supposed to improve it. It is much easier to document negative outcomes. It is very difficult to measure “quality of care” and as such, the attempt by these authors was a good one. In order to operationalize this variable, the researchers made a valiant attempt to create a measure which would be both useful and meaningful for readers. It is a difficult concept in which to prove an increase, or even to quantify, and this paper shows just how difficult it can be.

Abstract: Increasing the use of e-consultation in primary care: Results of an online survey among non-users of e-consultation.

Nijland N., van Gemert-Pijnen J. E.W.C., Boer H., Steehouder M. F., and Seydel E. R.(2009). Increasing the use of e-consultation in primary care: Results of an online survey among non-users of e-consultation. International Journal of Medical Informatics 78(10), 688-703.

Slide show , Full text , Draft report

Abstract

Objective
To identify factors that can enhance the use of e-consultation in primary care. We investigated the barriers, demands and motivations regarding e-consultation among patients with no e-consultation experience (non-users).

Methods
We used an online survey to gather data. Via online banners on 26 different websites of patient organizations we recruited primary care patients with chronic complaints, an important target group for e-consultation. A regression analysis was performed to identify the main drivers for e-consultation use among patients with no e-consultation experience.

Results
In total, 1706 patients started to fill out the survey. Of these patients 90% had no prior e-consultation experience. The most prominent reasons for non-use of e-consultation use were: not being aware of the existence of the service, the preference to see a doctor and e-consultation not being provided by a GP. Patients were motivated to use e-consultation, because e-consultation makes it possible to contact a GP at any time and because it enabled patients to ask additional questions after a visit to the doctor. The use of a Web-based triage application for computer-generated advice was popular among patients desiring to determine the need to see a doctor and for purposes of self-care. The patients’ motivations to use e-consultation strongly depended on demands being satisfied such as getting a quick response. When looking at socio-demographic and health-related characteristics it turned out that certain patient groups – the elderly, the less-educated individuals, the chronic medication users and the frequent GP visitors – were more motivated than other patient groups to use e-consultation services, but were also more demanding. The less-educated patients, for example, more strongly demanded instructions regarding e-consultation use than the highly educated patients.

Conclusion
In order to foster the use of e-consultation in primary care both GPs and non-users must be informed about the possibilities and consequences of e-consultation through tailored education and instruction. We must also take into account patient profiles and their specific demands regarding e-consultation. Special attention should be paid to patients who can benefit the most from e-consultation while also facing the greatest chance of being excluded from the service. As health care continues to evolve towards a more patient-centred approach, we expect that patient expectations and demands will be a major force in driving the adoption of e-consultation.

CATCH-IT Draft: The Relationship between Electronic Health Record Use and Quality of Care over Time

Zhou L, Soran CS, Jenter CA, Volk LA, Orav EJ, Bates DW, Simon, SR. Relationship between EHR use and Quality of Care over Time. J Am Med Inform Assoc. Jul-Aug 2009;16:457– 464.

Abstract and initial discussion

CATCH-IT Final Report

The paper chosen for this particular CATCH-IT report is entitled, The Relationship between Electronic Health Record Use and Quality of Care over Time. It was written by the research team of Zhou, Soran, Jenter, Volk, Orav, Bates and Simon. This team consisted of the leading researchers in electronic health records (EHR) from Columbia and Harvard Universities. This paper can be considered a high quality paper because this group has conducted what little research there has been in this field. The results of this paper may have a potential impact on the decision of hospital administrators and clinicians to use EHRs in their respective practice settings. It was also picked because electronic health records are currently a very timely issue in political landscapes of Ontario and America.

This paper would be interesting for a number of stakeholders. It would interest researchers because it suggests that further research is required. There are also the quality measurement criteria that could be improved. It may also appeal to researchers because there is a potential for other quality of care data sets to be used to compare against individual clinician EHR usage. It would be of interest to politicians, especially the opposition because the conclusions drawn state that the use of EHR is not associated with improved healthcare outcomes. This can be used as an argument for government excess or spending. The media may also use the results of this study, especially in Ontario, to show that there may not be any inherent value in having an EHR at all. However, since this paper only discusses the quality aspect of care, this may not be as much of a disincentive to stop the investment in EHRs.

Some of the background information drawn for this paper is the methodology for the clinician EHR usage survey. To obtain this (and the survey) one would have to check the reference list and contact the author of that particular study which only measured physician EHR usage. It was noted upon reading the reference paper by Simon et al. that there were many physicians who had moved, were deceased or were no longer practicing medicine who received the survey.

This article studied the use of EHR and the quality of care given by clinicians in various practices settings. The study employed the use of two data sets. It is of interest to note that both data sets used are secondary data and were not created for the purpose intended by the study authors. One survey measured physicians’ adoption and use of EHR in the state of Massachusetts. It used an eight page questionnaire with a stratified random sample, i.e., hospital-based vs. large vs. rural practices, by specialty, etc. Sample weights were used to adjust for overall representation. For example, if there was a total of 70 physicians, and only 50 responded, the authors would perform an adjustment calculation, i.e., 50/70 multiplied by each answer to represent the entire population. The second data set was statewide data on physicians’ quality of care as indicated by their performance on widely used quality measures. The secondary data set was drawn from a HEDIS database. A HEDIS database is populated using insurance claims data. There were certain exclusion criteria set for the HEDIS data which made respondents younger, more recently graduated, in smaller practices with more patient visits and more likely to be female. These two data sets were integrated and linked for each physician. The outcome measures used were core EHR functions and associated features (i.e., health information and data, result management, order entry and management, decision support, and electronic communication and connectivity) as well as the HEDIS quality measuses which were aggregated into six clinical categories (i.e., asthma care, behavioural and mental health, cancer screening, diabetes care, well child and adolescent visit, and women’s health). A suggestion for future health outcome measures would be to use clinical guidelines for measuring quality. Also, the use of a feature such as decision support in this type of measure is misleading. The extent of decision support varies as well as the functionality as this feature has progressed from 2001 through 2005.

As mentioned, this study examined the cross-sectional relationship between having an EHR and concurrent indicators of quality of care. The authors carried out a longitudinal analysis to study the trend of EHR adoption and usage as well as to examine the association between the duration of EHR usage and quality of care. They compared the HEDIS quality measures of respondents to the length of time using an HER. The results show that EHR adoption increased over time. For the purposes of this study, adoption was equated to the availability of an EHR in a particular practice. This may not be the case. There can be physician who have an EHR available yet choose not to use it. The results also showed that the availability and use of EHR core functions increased. The usage of specific features was measured and presented in the paper as used most of the time, some of the time or none of the time. There was no statistical difference between EHR users and non-users. Finally, and most importantly, usage of an EHR was not related to increased quality of care.The results are valid. This paper addressed the research question it proposed from the outset. The data used shows that there is no clear connection between EHR usage and quality of care. However, it should be noted that the results are shown with adjusted values. It should be recognized that it is difficult to conduct these types of studies. There are measurement issues, one cannot prove correlation can be equated to causation and there are adjustments for variables. Initially, health care quality cannot be associated with systems. It is much easier to document negative outcomes.

There were only two references cited for relationship of EHR use and quality of care. This would seem to indicate a need for more research to be conducted in this field. However, the difficulties with conducting this type of research have already been explained. The implications for policy makers would be that there may be a need to pay more for higher quality care. This may ensure that physicians are using EHRs to their full capacity. This can be achieved through education and workflow transformation. There may need to be stronger incentives or more extensive programs to support physician office transformation. One strong argument the authors suggest is that The 2005 EHR adoption rate will need to triple in less than a decade (for MA) for 100% physician EHR usage. This represents a challenge for health care policy.

CATCH-IT DRAFT REPORT: Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension

Introduction

Globally overweight and obesity, representing at least 300 million clinically obese persons, pose a major risk for chronic diseases, including type 2 diabetes, cardiovascular disease, hypertension and stroke (1). Increasingly, health care planners are looking for affordable strategies(2) inclusive of leveraging the use of information and communication technology (ICT) as a viable tool to support patients’ self-management of these diseases. This paper (3) is one of the most recent studies, which evaluates the use of both cellular phone and internet as an intervention in the relationship between measures of self-efficacy and managing obese hypertension.

One of the authors of this paper, Hee-Sung Kim, has experience with previous research studies using ICT for chronic disease management focusing on diabetes care in publications dated back to 2003. No further work in this area is established for the other authors.
Objectives

The aim of the study is to evaluate whether an intervention using a short message service (SMS) by cellular phone and Internet would improve blood pressure (BP), weight control, and serum lipids of obese patients with hypertension during 8 weeks(3). The authors cite the rationale for this as “no study has been done to test the direct efficacy of the cellular phone or internet-based system” on improving of these measures for hypertension”. Logan etal, 2007 presented a paper on use of both inventions, which is referenced by authors making this not a novel research.

Methodological Issues

Firstly, the intervention is not clearly defined, “.. intervention group were requested to record their blood pressure and body weight in a weekly web based diary through the Internet or by cellular phones.” (3) No justification is given for the choice of SMS or descriptive given of how the patient would input information from the cellular phone. In addition given that the data is self-reported, there is no indication of any objective ways to confirm if any of the data reported on which the decision for the alerts were sent.

Secondly, omission of the actual population size of the data collected from which participants are drawn. Associated with this is the potential for selection bias, as it is unclear as to who selected the control group and what other factors might have been considered in addition to matching the age, sex, systolic BP, diastolic BP and body weight to the intervention group at the same department. This appears to pose the single most important threat to internal validity, as it is possible to pull patient records with no change in the clinical outcome variables, unless this was a blinded process.

Thirdly, usage data of the intervention is omitted. Review done on related articles presenting studies with the use of ICT intervention such as by Patrick etal 2009, Raab etal 2009, Cocosila etal 2009, Morak etal 2008, Logan etal, 2007, and Kwon etal 2004, report results in addition to the clinical outcome. Data expected are those such as mean number of logon times per patient per day, alerts sent from both SMS and internet, entries for clinical measures such as blood pressure, weight, drug entries and most frequent comments over the period.

Fourthly, given that the study incorporates the behavioral pattern of patients, the theoretical approach used for this study is not stated explicitly for self-efficacy(4).

Discussion

This report is contributing to the body of literature on behavioural change through online intervention which is still a relatively new area of research. However, the results must be taken with caution base on the fundamentally flawed methodological issues that is associated with the research.

Hee-Seung Kim has authored one publication(5) and co-authored eleven publications. In one of the earlier researches co authored in 2004, “Establishment of Blood Glucose Monitoring System Using the Internet”(6), the ICT intervention is clearly defined with inclusion of images of the screen and reporting of the results as seen in related researches(7)(8)(9)(10).

It is quite noticeable that subsequent studies co-authored have been the usage of the same web-based diary intervention research from the same institution, which has been extended into variations of research papers on diabetes management in conjunction with SMS cellular phone. This paper under review has portions of the writing that are verbatim with blind application of parts of study with no evidence of results or relevance. How much of this is original work and could this have lead to the decrease in the quality of the reporting and exclusion of the pertinent information alluded to in the methodological issues?

The authors state amongst the limitations cited that some patients may not had access to a computer or able to use a cell phone, despite the inclusion criterion that they "should be able to input data into the website and have their own cellular phone" (3). Clearly, this is conflicting and poses additional difficulty on the validity of assessing “direct efficacy”.

Another critical element missing from this and past reports of similar study by the authors is the lack of information regarding the patients’ perspective on the ease of use, acceptance and effectives of the interventions. It would be valuable to know the extent to which patients find the ICT interventions to be helpful in disease self-management, increased self-efficacy, and treatment adherence, as the technology becomes an integral part of people's everyday life. This information would also help to inform future research and long-term planning.

Finally, the authors conclude, “the intervention using SMS of cellular phone and Internet improved blood pressure, body weight, waist circumference, and HDL-C at 8 weeks in obese hypertensive patients (3).” However, given the number of concerns regarding the internal validity, no method of accounting for alternative explanations opportunity for researchers to introduce bias in the interaction with patients and lack of generalization due to low sample size; these will limit greatly the level of confidence in all inferences that might be drawn from this study deeming the results not valid. Overall, the poor quality of reporting has detracted from the goal of the study.

Acknowledgement

Thank you to the professors and fellow graduate students of the 2009 CATCH-IT Journal Club at the University of Toronto, for their helpful and insightful discussion and comments that contributed to this report.


Questions to the Authors

1. What is the usage data of the cellular phone and internet intervention such as daily frequency response rate per patient per measure, number of alerts sent, and number of entries for clinical measures over the 8 weeks period?

2. What measurement is used to determine self-efficacy in the adherence to control of hypertension?

3. What is the rationale for exclusion of patients that changed medication during the period of the interventions and how many persons were excluded due to this in the intervention and control group?

4. What specific differences are identified using the paired t-test with Bonferroni correction and why use ANOVA rather than t-test when comparing the groups?

5. Why is the patient’s perspective not included on the usability and effectiveness of the intervention?


References


1. World Health Organization. Obesity and overweight. [Online].; 2003 [cited 2009 October 2. Available from: http://www.who.int/dietphysicalactivity/publications/facts/obesity/en/.

2. Prentice A. The emerging epidemic of obesity in developing countries. Int. J. Epidemiol. 2006; 35: p. 93–99.

3. Park M, Kim H, Kim K. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. Int J Med Inform. 2009 Oct; 78(10): p. 704-10.

4. Kim H. A randomized controlled trial of a nurse short-message service by cellular phone for people with diabetes. Int J Nurs Stud. 2007 July; 44(5): p. 687-692.

5. Kwon H, Cho J, Kim H, Song B, Ko S, Lee J, et al. Establishment of Blood Glucose Monitoring System Using the Internet. Diabetes Care. 2004 February; 27(2): p. 478-483.

6. Logan AG, McIsaac WJ, Tisler A, Irvine MJ, Saunders A, Dunai A, et al. Mobile Phone-Based Remote Patient Monitoring System for Management of Hypertension in Diabetic Patients. Am J Hypertens. 2007 September; 20(9): p. 942-948.

7. Morak J, Schindler K, Goerzer E, Kastner P, Toplak H, Ludvik B, et al. A pilot study of mobile phone-based therapy for obese patients. J Telemed Telecare. 2008; 14(3): p. 147-9.

8. Cocosila M, Archer N, Haynes RB, Yuan Y. Can wireless text messaging improve adherence to preventive activities? Results of a randomised controlled trial. Int J Med Inform. 2009 April; 78(4): p. 230-238.

9. Patrick K, Raab F, Adams M, Dillon L, Zabinski M, Rock C, et al. A Text Message–Based Intervention for Weight Loss: Randomized Controlled Trial. J Med Internet Res. 2009; 11(1 ): e1.

10. Anhoj J, Moldrup C. Feasibility of Collecting Diary Data From Asthma Patients Through Mobile Phones and SMS (Short Message Service): Response Rate Analysis and Focus Group Evaluation From a Pilot Study. J Med Internet Res. 2004 Oct–Dec; 6(4): p. e42.

Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial

Holbrook et al. Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. CMAJ. 2009 Jul 7;181(1-2):37-44

Full Text Draft Report Slideshow

Background: Diabetes mellitus is a complex disease with serious complications. Electronic decision support, providing information that is shared and discussed by both patient and physician, encourages timely interventions and may improve the management of this chronic disease. However, it has rarely been tested in community-based primary care.

Methods: In this pragmatic randomized trial, we randomly assigned adult primary care patients with type 2 diabetes to receive the intervention or usual care. The intervention involved shared access by the primary care provider and the patient to a Web-based, colour-coded diabetes tracker, which provided sequential monitoring values for 13 diabetes risk factors, their respective targets and brief, prioritized messages of advice. The primary outcome measure was a process composite score. Secondary outcomes included clinical composite scores, quality of life, continuity of care and usability. The outcome assessors were blinded to each patient’s intervention status.

Results: We recruited sequentially 46 primary care providers and then 511 of their patients (mean age 60.7 [standard deviation 12.5] years). Mean follow-up was 5.9 months. The process composite score was significantly better for patients in the intervention group than for control patients (difference 1.27, 95% confidence interval [CI] 0.79–1.75, p <> of patients in the intervention group, compared with 42.6% (110/258) of control patients, showed improvement (difference 19.1%, p <> more variables with improvement for the intervention group (0.59, 95% CI 0.09–1.10, p = 0.02), including significantly greater declines in blood pressure (–3.95 mm Hg systolic and –2.38 mm Hg diastolic) and glycated hemoglobin (–0.2%). Patients in the intervention group reported greater satisfaction with their diabetes care.

Interpretation: A shared electronic decision-support system to support the primary care of diabetes improved the process of care and some clinical markers of the quality of diabetes care. (ClinicalTrials.gov trial register no. NCT00813085.)

Goud et al. Effect of guideline based CDS

Goud R, de Keizer NF, ter Riet G, Wyatt JC, Hasman A, Hellemans IM, Peek N. Effect of guideline based computerised decision support on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation. BMJ. 2009;338:b1440.

Full Text

Abstract

OBJECTIVE: To determine the extent to which computerised decision support can improve concordance of multidisciplinary teams with therapeutic decisions recommended by guidelines.

DESIGN: Multicentre cluster randomised trial.

PARTICIPANTS: Multidisciplinary cardiac rehabilitation teams in Dutch centres and their cardiac rehabilitation patients.

INTERVENTIONS: Teams received an electronic patient record system with or without additional guideline based decision support.

MAIN OUTCOME MEASURES: Concordance with guideline recommendations assessed for two standard rehabilitation treatments-exercise and education therapy-and for two new but evidence based rehabilitation treatments-relaxation and lifestyle change therapy; generalised estimating equations were used to account for intra-cluster correlation and were adjusted for patient's age, sex, and indication for cardiac rehabilitation and for type and volume of centre.

RESULTS: Data from 21 centres, including 2787 patients, were analysed. Computerised decision support increased concordance with guideline recommended therapeutic decisions for exercise therapy by 7.9% (control 84.7%; adjusted difference 3.5%, 95% confidence 0.1% to 5.2%), for education therapy by 25.7% (control 63.9%; adjusted difference 23.7%, 15.5% to 29.4%), and for relaxation therapy by 25.5% (control 34.1%; adjusted difference 41.6%, 25.2% to 51.3%). The concordance for lifestyle change therapy increased by 3.2% (control 54.1%; adjusted difference 7.1%, -2.9% to 18.3%). Computerised decision support reduced cases of both overtreatment and undertreatment.

CONCLUSIONS: In a multidisciplinary team motivated to adopt a computerised decision support aid that assists in formulating guideline based care plans, computerised decision support can be effective in improving the team's concordance with guidelines. Therefore, computerised decision support may also be considered to improve implementation of guidelines in such settings.

TRIAL REGISTRATION: Current Controlled Trials ISRCTN36656997.

Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension

Park MJ, Kim HS, Kim KS. Cellular phone and Internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. International journal of medical informatics. 2009 Oct;78(10):704-10.
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ABSTRACT

Purpose: The present study evaluated whether an intervention using a short message service (SMS) by cellular phone and Internet would improve blood pressure, weight control, and serum lipids of obese patients with hypertension during 8 weeks.

Methods: This is a quasi-experimental design with pre- and follow-up tests. Participants were recruited from the family medicine outpatient department of tertiary care hospital located in an urban city of South Korea. Twenty-eight patients were assigned to an intervention group and 21 to a control group. The goal of intervention was to bring blood pressure, body weight, and serum lipids levels close to normal ranges. Patients in the intervention group were requested to record their blood pressure and body weight in a weekly web based diary through the Internet or by cellular phones. The researchers sent optimal recommendations as an intervention to each patient, by both cellular phone and Internet weekly. The intervention was applied for 8 weeks.

Results: Systolic (SBP) and diastolic blood pressures (DBP) significantly decreased by 9.1 and 7.2mm Hg respectively at 8weeks fromthe baseline in the intervention group (p < 0.05). However, after 8weeks from the baseline both SBP and DBP in the control group had not changed significantly. Yet, There were significant mean decreases in body weight and waist circumference by 1.6 kg (p < 0.05) and 2.8cm (p < 0.05) in the intervention group, respectively. In the control group increases in body weight and waist circumference (p < 0.05) mean changes were also significant. High density lipoprotein cholesterol (HDL-C) significantly increased, with a mean change of 3.7 mg/dl at 8weeks frombaseline in the intervention group (p < 0.05). The mean change of HDL-C in the control group was, however, not significant.

Conclusion: During 8 weeks using this web-based intervention by way of cellular phone and Internet SMS improved blood pressure, body weight, waist circumference, and HDL-C in patients with obese hypertension.