 To test the efficiency and cost-effectiveness of interventions aimed at enhancing laboratory monitoring of medication.

 Patients of a not-for-profit, group-model HMO were randomized to 1 of 4 interventions: an electronic medical record reminder to the clinician, an automated voice message to patients, pharmacy-led outreach, or usual care. Patients were followed for 25 days to determine completion of all recommended baseline laboratory monitoring tests. We measured the rate of laboratory test completion and the cost-effectiveness of each intervention. Direct medical care costs to the HMO (repeated testing, extra visits, and intervention costs) were determined using trial data and a mix of other data sources.

The average cost of patient contact was $5.45 in the pharmacy-led intervention, $7.00 in the electronic reminder intervention, and $4.64 in the automated voice message reminder intervention. The electronic medical record intervention was more costly and less effective than other methods. The automated voice message intervention had an incremental cost-effectiveness ratio (ICER) of $47 per additional completed case, and the pharmacy intervention had an ICER of $64 per additional completed case.

 Using the data available to compare strategies to enhance baseline monitoring, direct clinician messaging was not an efficient use of resources. Depending on a decision maker’s willingness to pay, automated voice messaging and pharmacy-led efforts can be efficient choices to prompt therapeutic baseline monitoring, but direct clinician messaging is probably a less efficient use of resources.

Patients of a not-for-profit, group-model HMO were randomized to 1 of 4 interventions: an electronic medical record reminder to the clinician, an automated voice message to patients, pharmacy-led outreach, or usual care.

The electronic medical record intervention was more costly and less effective than other

Depending on a decision maker’s willingness to pay, pharmacy-led outreach or automated

	voice messages to patients can be cost-effective methods to enhance laboratory monitoring of

	medications at initiation of therapy compared with usual care.

Recent studies indicate that laboratory monitoring of medications at initiation of therapy is below the level recommended by guidelines, with as many as 39% of patients not receiving recommended testing.

 Lack of monitoring is a concern because of potential adverse events (eg, hyperkalemia associated with inhibitors of angiotensin) and because of failure to achieve therapeutic benefit due to inadequate blood levels of medication. Additionally, failure to establish baseline levels makes it difficult to determine a patient’s trends in laboratory values.

These concerns regarding patient safety and clinical effectiveness have led researchers to test methods to enhance laboratory-based medication monitoring. Several types of interventions (including pharmacyled efforts, electronic reminders to clinicians, and automated telephone call reminders to patients) to improve laboratory monitoring of medications at therapy initiation have been effective in randomized trials.

 Although all these interventions improve monitoring, the most efficient intervention methods are not clear, and no economic analyses have been done to inform policy makers in this area. Some efforts may be particularly resource intensive, but could be worth the added expenditure when the potential adverse outcome is severe. Without careful analysis of the balance between costs and benefits, one cannot determine which (if any) interventions ought to be funded by healthcare payers. The efficiency of alternative approaches to therapeutic monitoring is of growing importance to healthcare providers as this monitoring is now a focus of quality measurement.

 To help with decision making regarding laboratory-monitoring interventions, we undertook a preplanned costeffectiveness analysis of a randomized trial that tested several interventions aimed at enhancing laboratory monitoring of medication.

Complete details of the trial design are available elsewhere.

 The study was conducted at a not-for-profit, group-model HMO and was approved by its institutional review board. All HMO patients who had not received baseline laboratory tests (defined as within 6 months before or 5 days after a newly dispensed study medication) were randomized to 1 of 4 conditions: an electronic medical record reminder to the patient’s primary care provider (EMR arm), an automated voice message to patients (AVM), pharmacy team outreach (Pharmacy), or usual care (UC) (

). In the EMR intervention, a patient-specific electronic message was sent to the primary care clinician from the chair of the HMO’s patient safety committee stating that computer records indicated the patient had received a new medication, that laboratory monitoring was recommended, and the patient had not received the test(s) between 6 months before and 5 days after the dispensing. The message referenced internal and external guideline resources, recommended specific tests, and provided a sample letter the clinician could send to the patient. The AVM intervention included telephone messages advising the patient that laboratory tests were required for a medication the patient had received; the patient was advised that the testing had been ordered and could be completed at any HMO laboratory. The Pharmacy intervention began with a telephone call from a nurse in the pharmacy department to the patient to encourage laboratory testing. If the nurse successfully contacted the patient, a follow-up letter reminded the patient to obtain the laboratory test(s). If telephone contact was not successful, the nurse sent a letter suggesting that the patient go in for testing. If patients had questions or concerns about their medication during the contacts, a pharmacist was available for consultation.

Study medications (and lab tests required) were angiotensinconverting enzyme inhibitors or angiotensin receptor blockers (serum creatinine, serum potassium), allopurinol (serum creatinine), carbamazepine (aspartate aminotransferase [AST] or alanine aminotransferase [ALT], complete blood count, serum sodium), diuretics (serum creatinine, serum potassium), metformin (serum creatinine), phenytoin (AST/ALT, complete blood count), pioglitazone (AST/ALT), potassium supplements (serum potassium, serum creatinine), statins (AST/ALT), and terbinafine (AST/ALT, serum creatinine). The primary outcome was laboratory test completion, defined as the proportion of patients with all recommended baseline laboratory monitoring tests completed at 25 days after the intervention date. In the year before randomization, the laboratory-monitoring rates at the initiation of therapy (those who had initiated a study medication and had completed all recommended baseline laboratory testing) were similar in the study groups (about 60%). Other characteristics of the study groups also were similar, but the AVM group had a smaller proportion of female primary care physicians (24% vs ~40%). A total of 961 patients were included in the clinical trial. By day 25 after the intervention, 22.4% (53 of 237 patients) in the UC arm, 48.5% (95 of 196 patients) in the EMR arm, 66.3% (177 of 267 patients) in the AVM arm, and 82.0% (214 of 261 patients) in the Pharmacy arm all had completed recommended monitoring (P <.001). A total of 72 abnormal test results were found among the 961 patients (7.5%).

We followed best practice in economic evaluation as outlined by the US Public Health Service.

 Our economic analysis examined the incremental cost per additional case completed (defined as enrollees that had all guideline-specified laboratory tests completed) and the incremental cost per abnormal case detected. We calculated the incremental cost-effectiveness ratio (ICER) by dividing the difference in cost by the difference in cases completed (or abnormal cases detected); interventions with lower ICERs are a better value for the money. Interventions with a higher ICER also may be cost-effective, depending on a decision maker’s willingness to pay for each additional unit of effect. Interventions were ranked on cost, and dominated options (ie, more costly but less effective) were identified. To account for the uncertainty due to sampling variation in cost-effectiveness analysis, we plotted cost-effectiveness acceptability curves5; these curves show the probability of each intervention being cost-effective at a given willingness to pay for an additional completed case (or abnormal case detected). All analyses were conducted using STATA release 9.0 (StataCorp, College Station, TX) and Microsoft Excel 2003 (Microsoft, Redmond, WA).

Most of the cost data were collected directly from the trial, with some expert opinion based on formal data-gathering techniques as described below. The perspective of the analysis was the HMO. Thus, we included only direct medical care costs incurred by the HMO. The scope of the analysis included the costs within the 25 days after the intervention of (1) all recommended laboratory tests (including repeated testing), (2) extra visits associated with abnormal tests (validated from pharmacist chart review), and (3) performing the intervention. The analysis does not include potential offsets of poor outcomes averted (eg, lactic acidosis, liver toxicity) because those data were too sparse to answer those questions effectively. Also excluded from the analysis were development costs and patient costs, like travel time and copayments. To maintain consistency with the efficacy analysis, the primary outcome was the cost per completed case within 25 days of dispensing, with a secondary analysis of the cost per addition al enrollee with 1 or more abnormal laboratory tests within 25 days of dispensing.

 details the unit costs (ie, prices), activities, data sources, and resource assumptions used in the analysis. To improve generalizability to other systems, salary costs were taken from sources reflecting the prevailing wage rate in Portland, Oregon, with a fringe benefit rate of 30% and overhead rate of 20% added to fully allocate the costs. Laboratory testing costs come from the HMO’s laboratory accounting system and include patient intake, phlebotomy, testing, and reporting. Mailing costs were applied based on estimates for bulk mailing, and costs for clinic visits came from the HMO’s cost structure.

Resources used in the performance of all the interventions included chart review to ensure patient eligibility, tracking systems for patient follow-up, and noting the intervention delivery in the patient’s medical record. Additional tasks were study arm specific. In the Pharmacy arm they included time for mailings and outreach phone calls and their documentation. The AVM intervention required time to upload files. The cost of maintaining the automated telephone system was embedded in the vendor charge to the HMO. The EMR intervention costs included nurse time to send the message and clinician followup activities. Because existing EMR functionality was used to provide the EMR messages, no incremental programming resources were necessary to provide the intervention. The time (in minutes) taken to complete these tasks was recorded for a sample of patients in the Pharmacy, AVM, and EMR arms. To establish patient-level resource use, each patient in the appropriate arm was assigned an imputed value randomly from the sample, preserving the sample’s underlying (observed) distribution. Analyst time to create and maintain the patient lists from automated data were taken from the trial.

In the base-case analysis, formal interviews were undertaken with 13 HMO clinicians to estimate time (in minutes) for all aspects of responding to the EMR message: reading the message, ordering tests, and review and follow-up (for normal and abnormal results separately) on laboratory results. We then imputed patient-level resource use for these items by randomly selecting from a triangular distribution, using the expert’s estimates of the mode and range. Because they came from expert opinion, we undertook sensitivity analyses by assigning the low estimates and, separately, the high estimates of time required for ordering, review, and follow-up on normal and abnormal test results. Additionally, because of uncertainty regarding the frequency and type of contact for patients in the EMR arm (eg, telephone, letter, during visits), we undertook a sensitivity analysis that reduced the cost of contact in that arm.

presents the resources used per patient by arm (ie, unit costs from Table 1 multiplied by actual units of use). The increase in laboratory testing associated with each of the interventions is evident from changes in the average cost of testing ($18.65 for UC, vs $26.69, $32.44, and $40.93 for EMR, AVM, and Pharmacy, respectively); the cost for review and follow-up of both normal and abnormal test results followed a similar pattern. The average cost of patient contact (including chart review, notification tracking, and charting) was $5.45 in the Pharmacy arm. The EMR arm used about $7 ($3.18 + $3.77) for patient contact, while the AVM arm cost for patient contact came to $4.64 ($3.55 + $1.09).

 shows that enhanced effectiveness comes with increases in total cost. Pharmacy, the intervention with the greatest proportion of completed cases at 82 per 100 patients, is also the most expensive at $5160 per 100 patients, whereas the AVM arm yielded 66 completed cases per 100 patients at a cost of $4159. The EMR arm was “dominated,” because using a mix of UC (eg, for 40% of patients) and AVM (eg, for 60% of patients) would be both less expensive and more effective; the EMR strategy is therefore a suboptimal choice. The AVM intervention had an ICER of $47 per additional completed case; the Pharmacy arm had an ICER of $64 per additional completed case. At a lower level of willingness to pay for an additional completed case (eg, $40), UC was the intervention with the highest probability of being cost-effective; as willingness to pay increased, however, other interventions became more likely to be cost-effective.

 shows an examination of the cost-effectiveness of finding abnormal cases. There was a similar pattern of increasing effectiveness at higher cost, and the EMR intervention method was again dominated. As with completed cases, at a lower level of willingness to pay for finding an abnormal case (eg, $400), UC had the highest probability of being cost-effective, but above $600 AVM and Pharmacy became more likely to be cost-effective. The sensitivity analysis based on low and high estimates of time used in ordering, reviewing, and follow-up of normal and abnormal tests  showed no difference in ICER ranking and nearly identical results overall (eg, ICERs of $44 and $50 for the AVM arm vs $47 for the base-case analysis). The sensitivity analysis on the cost of contact for patients in the EMR arm revealed that, even if the cost of patient contact was reduced to zero (eg, the marginal cost might be close to zero if all contact took place during visits), the EMR arm would never be the optimal strategy.

 shows the cost-effectiveness acceptability curves. For example, at a willingness-to-pay level for a completed case between $47 and $64, AVM had the highest probability of being cost-effective, but the Pharmacy intervention had the highest probability of being cost-effective at greater levels. There is considerable uncertainty in the estimates of cost-effectiveness, particularly for abnormal case detection. For example, at levels of $800 and higher AVM had a probability of being cost-effective of about 0.32, while Pharmacy was cost-effective with a probability of about 0.50.

We found that direct-to-clinician messaging (EMR) to prompt therapeutic baseline monitoring is probably not an efficient use of resources. But our analysis indicates that depending on a decision maker’s willingness to pay for a completed case, both AVM and Pharmacy can be efficient choices. Deciding which choice is optimal requires additional information about the harms prevented from completing baseline monitoring; our analysis did not include these costs because of limitations in study budget and design. However, it is worth noting that compared with the ICER for the AVM arm, the ICER for the Pharmacy arm was almost 40% greater for the “cases completed” analysis, but was less than 10% greater for the “abnormal cases detected” analysis, suggesting that the relative efficiency of the Pharmacy intervention may increase when considering harms.

As noted, our findings are most useful for an efficiency comparison between the alternative methods in the analysis. The findings give less guidance to help with the decision about what (if any) program should be adopted; a cost-benefit or cost-utility analysis would be needed to answer that question. But an informal analysis can be made using the data in Table 3 and making assumptions about the cost and probability of events that might be avoided by a monitoring program. For example, if one assumes that the cost of an event avoided would be $10,000 (ie, short inpatient stay) and the probability of the event for those with an abnormal test is 5%, the willingness-to-pay amount to avoid the event would be at least $500 (ie, $10,000 × 5%); this example does not incorporate potential losses in health-related quality of life. Empirical work on the probability of abnormal tests leading to adverse events, the cost of those events, and the health benefits gained is necessary to further inform decision makers’ choices in this area. Reports of simulation modeling on the economics of preventing medication errors using information technology and pharmacy personnel indicate that health-related quality-of-life benefits (from avoiding medican tion errors) are a critical parameter; their inclusion reversed the estimates from negative to positive net benefits.6

Our intervention did not differentiate patients with regard to potential for poor outcomes; rather it was applied to all patients with a missing laboratory test at baseline. Another approach would be to administer the intervention to patients who are predicted to be at particularly high risk of poor outcomes. By targeting high-risk patients, the proportion of patients with a greater likelihood of adverse events (given an abnormal test) would increase. Such an approach might increase the efficiency of the strategies but would require identification of those patients, perhaps by using a risk score approach.

The clinical trial on which we based our economic analysis showed that each of the 3 interventions was superior to UC in improving monitoring at 25 days; patients in the EMR arm were 2.5 times more likely to have recommended monitoring.

 But when we included costs (ie, increase in clinician time associated with contacting the patient and follow-up), the EMR arm was shown to be relatively inefficient. Off-loading these tasks to others enhances the overall effects and lowers costs. We included the effort required by clinicians to review laboratory test results in our cost estimates. Another approach would have been to assume that clinicians can absorb the cost of reviewing the extra tests into their slack time. But as suggested by best practice guidelines in economic evaluation,

 we estimated the “opportunity cost” of providing the interventions. The concept of opportunity cost requires measurement of all resources necessary to produce an intervention, even if those resources may be viewed as free (eg, the effort required to review laboratory tests); this concept recognizes that doing one thing means that another thing cannot be done (because that opportunity is lost).

We found relatively high levels of certainty associated with willingness to pay for an additional completed case. At a willingness-to-pay level of less than $40 the probability of UC being the most cost-effective was about 0.9, and at a willingness-to-pay level greater than about $85, the Pharmacy intervention was the most cost-effective with a probability of 0.9. With intermediate levels of willingness to pay for a completed case (ie, $50-$65) AVM was the most cost-effective, but with less certainty (probability about 0.6). On the other hand, we found considerable uncertainty with the abnormal case detection analysis. No single intervention showed costeffectiveness with probability greater than 0.5 at a willingness to pay between $450 and $850. The uncertainty estimates are loosely analogous to a wide confidence interval (see van Hout et al

 for further details) and point out the lack of precision in our estimates. More precise estimates require further data collection.

Our results were not sensitive to the estimates of time required to order, review, and follow-up on tests, suggesting that additional data collection should focus on other areas like estimating the potential cost-offsets from avoiding poor outcomes.

Some items we included in the performance of these interventions, for example, the individual chart review of each patient prior to intervention to ensure eligibility (ie, we did not rely entirely on electronic data), may not have been strictly necessary. Excluding these potentially protocol-driven costs may change the relative cost-effectiveness of the interventions. We felt that this was an important feature to include, however, because the effectiveness of the intervention was based on that level of rigor, and because the HMO would likely not be willing to implement any of the programs without that double-check. Additionally, we included patient contact costs in the EMR arm for all patients, but this may have overcounted costs in that arm to the extent that the rate of contact was lower. But our sensitivity analysis on that variable indicated that even if the contact costs were reduced to zero the EMR arm would never have been the optimal strategy.

The clinical trial allowed us to evaluate strategies that would off-load clinicians’ work (ie, AVM and Pharmacy arms) as compared with more intrusive strategies (the EMR arm). Because we did not set out to balance resources available in the intervention arms, we cannot determine whether the effect and cost difference we observed was solely based on the intervention delivery mechanism, the intensity of the intervention, or other factors. Also, although a laboratorymonitoring protocol was available and communicated to all clinical staff, clinicians in the EMR arm may have been more likely to exercise clinical judgment on a case-by-case basis and thus not intervene for every patient. Our analysis assumes that all patients who received interventions were at the same risk of poor outcomes. Because we did not measure outcomes in our analysis, to the extent that the clinical judgment exercised by EMR clinicians improved the likelihood of interventions for high-risk patients (thus improving their group’s potential to benefit), our analysis underestimated the true effect in the EMR arm.

Our study has limitations. The clinical trial on which it was based was undertaken in a single HMO, so the effect of the interventions (especially EMR) may not be completely generalizable to other practice settings. Additionally, the parent study was not designed to determine whether the interventions led to improved detection of abnormal baseline laboratory test results or significant changes in patient care and outcomes. Our findings in this cost-effectiveness analysis help to put the clinical trial results in a decision-making context. We think a hybrid approach to the intervention may be a particularly useful approach. For example, one that includes AVM followed by Pharmacy outreach for nonresponders may prove to be efficient.

The efficient use of health information technology is of great interest to clinicians and healthcare payers, and our study is one of the first to estimate the relative cost-effectiveness of these technologies. Using automated telephone technology has been shown to have promise in many clinical areas, for example post-inpatient discharge,

 enhancing immunization rates in children,

 but the cost-effectiveness of these systems is less clear. The use of automated medical records has been the subject of several systematic reviews.

 Like automated telephone technology, the EMR systems have promise, but the economic benefits are less certain. Our study shows that providing clinical decision support directly to clinicians using EMR technology is more costly and less effective than automated telephone calls for increasing laboratory monitoring. This finding is particularly of interest given (1) that we did not include costs for programming of EMR messages because existing EMR functionality was used and (2) the “sunk cost” of the EMR was not included in our analysis because from the HMO’s perspective the EMR technology was already owned. But it should be noted that the costs of chart review and tracking activities would likely have been substantially higher in a health system that lacked an EMR. Our study may serve as a cautionary example of when EMR-based intervention methods are not the most efficient choice.

Depending on one’s willingness to pay, both AVM and a pharmacy-led intervention may be efficient methods to enhance the level of laboratory monitoring of medications at initiation. Our analysis gives guidance, but further empirical work to estimate the cost and probability of adverse events associated with abnormal laboratory tests is important to further inform funding decisions.

: From the Center for Health Research (DHS, ACF, NAP, XY, MMR), Kaiser Permanente and Northwest Permanente (ACF), Portland, OR; the Institute for Health Research (MAR, DJM), Kaiser Permanente Colorado, Denver; Harvard Medical School (SRS, SBS) and Harvard Pilgrim Health Care (SRS, SBS), Boston, MA. Funding Source: The study was funded by Kaiser Permanente’s Garfield Memorial Fund.

: The authors (DHS, ACF, NAP, XY, MMR, MAR, DJM, SRS, SBS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: Concept and design (DHS, ACF, NAP, MMR, MAR, DJM, SRS, SBS); acquisition of data (DHS, ACF, XY, MMR); analysis and interpretation of data (DHS, ACF, NAP, XY, MMR, MAR, DJM, SRS, SBS); drafting of the manuscript (DHS, MMR); critical revision of the manuscript for important intellectual content (DHS, ACF, NAP, MAR, DJM, SRS, SBS); statistical analysis (DHS, NAP, XY, SBS); provision of study materials or patients (DHS); obtaining funding (DHS, ACF); administrative, technical, or logistic support (DHS, ACF, MMR); and supervision (DHS, ACF, SBS).

: David H. Smith, RPh, PhD, Center for Health Research, Kaiser Permanente, 3800 N Interstate Ave, Portland, OR 97227. E-mail: 

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Articles

CLINICAL

Although some interventions may enhance medication safety, an electronic medical record reminder to providers may not be an efficient use of resources.


Improving Laboratory Monitoring of Medications: An Economic Analysis Alongside a Clinical Trial

 To provide a systematic review of home telemonitoring for respiratory conditions and to present evidence on its effects in relation to data quality, patient medical condition, utilization of health services, feasibility and use, and economic viability.

Systematic review of the literature to identify peer-reviewed articles that reported effects of home telemonitoring for patients with respiratory conditions.

A literature search was conducted on Cochrane and MEDLINE (1966-2007) databases using the following keywords: pulmonary, respiratory, asthma, lung, telemonitoring, telecare, telehealth, telehomecare, and home monitoring.

Twenty-three studies were found that presented effects of telemonitoring for various pulmonary conditions. Good levels of data validity and reliability were reported, when assessed.However, little quantitative evidence exists about its effects on patient medical condition and utilization of health services. Positive effects on patient behavior were consistently reported. Only 2 studies performed a detailed cost analysis of this approach.

Home telemonitoring of respiratory conditions results in early identification of deteriorations in patient condition and symptom control. Positive patient attitude and receptiveness of this approach are promising. However, evidence on the magnitude of clinical and structural effects remains preliminary, with variations in study approaches and an absence of robust study designs and formal evaluations. Assessment of providers' attitudes toward telemonitoring and its effect on their workload is necessary.

A systematic review of the literature was conducted to evaluate existing evidence on the effects of home telemonitoring for respiratory conditions.

It represents a promising patient management approach that is well received by patients and supports early identification of deteriorations in patient condition and symptom control. Despite minimal evidence on its economic viability, early analyses show promising results and affordability of this approach.

However, evidence about the magnitude of its effects on patient condition, health services utilization, and economic viability remains preliminary, with variations in study approaches and an absence in most cases of robust study designs necessitating future formal evaluations. 

Chronic respiratory diseases represent a wide category of illnesses that have significant effects and burdens on society.

 These medical conditions necessitate education, self-monitoring, and close management

 to ensure better outcomes and to prevent exacerbations, complications, and potential death. Home telemonitoring presents an alternative for close follow-up of patients by ensuring timely transmission of clinical and physiologic data

 and by supporting prompt medical intervention before deteriorations occur in patients’ conditions.

 It has been used for the management and follow-up of several chronic diseases, including respiratory diseases.

 Asthma, chronic obstructive pulmonary disease (COPD), and pulmonary transplantation represent pulmonary conditions that have been studied in relation to home telemonitoring.

Given the variability in the study approaches and the relative novelty of home telemonitoring for pulmonary conditions, evidence remains scattered on its effects in relation to data quality, patient medical condition, utilization of services, feasibility and use, and economic viability. This study addresses this issue by providing a systematic review of the literature on research in the area of home telemonitoring for pulmonary conditions and by presenting recommendations for future research and studies in this area.

We performed a literature search on Cochrane Library and Ovid MEDLINE database (from 1966 to December 2007) to identify relevant studies that examined home telemonitoring for respiratory conditions. Telemonitoring, also referred to as telehomecare, involves the use of telecommunication technologies by patients for the timely transmission of data (eg, spirometric measures, vital signs, symptoms, and medications use) from home to a healthcare center. For the purpose of this study, the inclusion criteria consisted of (1) English-language publications in peer-reviewed journals and (2) studies involving patients with respiratory conditions and presenting results on telemonitoring effects.

We conducted the search using 4 keywords (pulmonary, respiratory, asthma, and lung) in conjunction with each of the following terms: telemonitoring, telecare, telehealth, telehomecare,and home monitoring. After eliminating redundancies, this search resulted in 52 articles. The abstracts were examined to identify articles that seemed potentially relevant to this study area, and 19 studies were deemed irrelevant based on the inclusion criteria. Two investigators (MJ, GP) further looked at each article to determine its appropriateness for inclusion in this review. During this process, they relied on the following exclusion criteria: (1) studies examining multipathologic groups of patients without separating patients with different conditions into various groups, (2) interventions that involved downloading of data during clinic visits or at the end of the study period, (3) studies that included only regular telephone calls by care providers without specialized home monitoring equipment, and (4) interventions that involved only in-home counseling or consultation sessions delivered via video visits. Twelve articles that seemed initially relevant to this review were excluded based on these exclusion criteria.

Finally, the bibliographies of the retained articles were examined to identify studies that were not originally captured in the search; 3 additional articles were retained. The final number that is included in this review is 24 articles; 2 articles referred to a single study, which led to the inclusion of a total of 23 studies in this review.

Overview of Home Telemonitoring Studies for Respiratory Conditions

, 7 studies targeted patients undergoing pulmonary transplantation, 12 studies focused on asthma, and 4 studies involved patients with COPD and other severe respiratory illnesses. In the 1990s, electronic diary and spirometer systems were mostly used for telemonitoring,

 introduced more sophisticated technologies such as handheld devices, digital technology, and wireless networks. Among the randomized trials included in this review, only one study

 used telephone short messaging services technology, while the other studies

 used Internet-based monitoring systems and electronic diaries for data transmission. Despite variability in the technologies used and their sophistication over the years, the modalities were similar across studies in relation to the types of transmitted data and frequency of transmission.

. Based on the classification by Jovell and Navarro-Rubio

 studies can be categorized according to the following 9 levels, primarily based on their study design, to demonstrate the rigor and strength of the evidence reported: (1) meta-analyses of randomized controlled trials (RCTs); (2) RCTs with large samples; (3) RCTs with small samples; (4) prospective studies with nonrandomized control groups; (5) retrospective trials with nonrandomized control groups; (6) cohort studies; (7) case-control studies; (8) descriptive studies, clinical series, and consensus techniques; and (9) case reports and anecdotes. We used this classification to evaluate the design quality of the 23 studies included in this review, which ranged from “poor” (higher classification levels) to “good to fair” (lower classification levels) (Table). Among 8 randomized trials, 7 studies

 were classified as level 3 and 1 study11 as level 2 (ie, good to fair). One study

 was level 6 (ie, fair to “good to fair”). Thirteen studies

 did not have a control group and were classified as level 8 (ie, poor). Later studies published in 2007 used randomized trial designs, which indicates a strong improvement in research design quality. Nevertheless, the sample size remained problematic, with most of the studies limited to small samples of patients.

Evidence on Home Telemonitoring Effects for Pulmonary Conditions

. As summarized in the eAppendix Table (available at 

), 13 studies (57%) presented information related to data and systems quality. Most studies that documented information about the reliability and validity of data were conducted in earlier years and involved patients undergoing pulmonary transplantation. The remaining studies that discussed technical issues mostly involved patients with asthma. Overall, good reliability and accuracy of data were demonstrated.

 evaluated and discussed the reliability and validity of data transferred electronically for telemonitoring, and a few studies

 verified and confirmed agreement between the spirometric measures obtained in a clinical setting and those taken at home. Technical problems and errors in measurements and functioning of instruments (eg, transmission and connection problems and cable damage) were identified in 6 studies,

 although researchers did not report them as hindering the telemonitoring process. These occasional technical difficulties and problems in data quality were not associated with specific types of systems or technology

 or with older systems used in earlier studies.

. Behavioral and psychological effects were elaborated in most of the articles (87%) (eAppendix Table). In general, most telemonitoring studies

 presented positive results in relation to acceptance of the systems and telemonitoring programs. Patients showed a positive attitude irrespective of differences in the composition, age groups, and size of the samples studied.

 Increased feelings of security and reassurance and control over their medical condition,

 better knowledge and awareness about their disease,

 and improved communication with health professionals

 that involved children with asthma reported that these patients showed positive attitudes toward the systems used, which also reminded them to take their medications. Improvements in their caregivers’ knowledge and quality of life were also identified.

However, adherence to data transmission varied among studies depending on data type, study duration, frequency of measurements and transmissions, and patient condition.

 Decreased adherence over time was observed in some studies

 that involved the transfer of numerous types of data (eg, spirometric data, vital signs, stress levels, and medications consumption), were performed over long durations, or required effort for transmitting data. The few studies

 that reported lower adherence rates than the others involved video transmission, requirement of several measurements, and transfer of numerous data types, including symptoms diaries.

Some technologies used in the telemonitoring studies may have had important effects on patient behavior. The use of complicated procedures for data transfer that involve video transmission

 or non—user-friendly monitors for recording symptoms

 was associated with poor compliance, as opposed to applications with reminders and prompts,

 which were associated with good patient adherence. Overall, ease of use and friendliness of the technology and monitoring equipment

may have contributed to good compliance among patients and their acceptance of the telemonitoring systems.

Clinical Effects on Patient Medical Condition

. Seventeen studies (74%) discussed clinical effects associated with the use of telemonitoring for patients with pulmonary conditions (eAppendix Table). However, these effects were not always quantitatively assessed. The most important clinical implication of home telemonitoring relates to the ability of healthcare professionals to contact patients in a timely manner and to perform necessary changes in their home management plans and therapies based on data received from patients before complications or exacerbations take place.

Clinical effects varied by type of medical condition

. In patients undergoing pulmonary transplantation, telemonitoring assisted in early identification and treatment of deterioration, organ rejection, and complications in the healthcare status of patients after the lung transplant procedure. For example, Finkelstein et al

 found a significant difference in the ability of home spirometry to early detect bronchiolitis obliterans syndrome, which is the most common chronic complication responsible for high mortality and significant morbidity after the first year of transplantation.

 Positive results were also reported in 2 other studies,

 although the latter study showed only mild sensitivity (63% for forced expiratory volume in 1 second and 39% for midexpiratory flow rate between 25% and 75% of forced vital capacity) of home telemonitoring in detecting acute allograft dysfunction. The only randomized trial study26 involving pulmonary transplant candidates before the procedure showed no preliminary effects of home telemonitoring

With respect to asthma and the other respiratory conditions, home telemonitoring assisted in the diagnosis of previously unrecognized health problems such as sleep-related breathing disorder among patients with COPD9 and the presence of tracheal neoplasm in patients with asthma.

 It also allowed identification of early signs of deterioration in patient health status

 Seven of 8 RCTs in this review involved patients with asthma, and most of these studies

 reported improvement in symptoms and asthma control because of telemonitoring. Significantly lower cough and night symptom scores,18 less activity limitation compared with patients without telemonitoring (48% lower odds),

 and greater improvement in asthma symptoms,

 lung function, quality of life, and airway responsiveness

 were observed among patients using telemonitoring. One study

 that found no significant difference in clinical outcomes between the intervention and control groups was limited to a small sample size (n = 5), and 2 other trials did not perform an in-depth evaluation of clinical outcomes

 or found that disease control was excellent among both the intervention and the control groups.

Health Services Utilization. Thirteen of 23 studies (57%) examined changes in the utilization of health services associated with the use of telemonitoring (eAppendix Table). However, no consistent and definitive evidence exists in relation to the extent to which home telemonitoring reduces the utilization of health services (eg, office visits, emergency department visits, and hospital admissions and length of stay). In most instances, no in-depth analyses were performed to identify these changes and to quantitatively determine their magnitude. Except for 2 randomized trials11,21 involving 253 and 134 patients, respectively, studies examining these effects were limited by their designs and small samples.

Economic Viability. Only 8 of 23 studies (35%) presented cost estimates related to the telemonitoring systems used

 Nevertheless, despite minimal existing evidence at this level, preliminary analyses showed promising results and affordability of this approach (eAppendix Table), especially with technology advancement and decreased cost over the years. Two studies

 that performed a detailed cost analysis of home telemonitoring presented evidence on its ability to produce savings. However, a cost-benefit analysis involving patients with asthma demonstrated that this approach is more costeffective among adults and suggested that telemonitoring will have limited cost-effectiveness unless the cost of the technology decreases.33

DISCUSSION AND RECOMMENDATIONS FOR FUTURE STUDIES

The critical nature of respiratory diseases, the continuously increasing prevalence of these conditions, and the subjective perception of patients vis-à-vis their pulmonary function and health status underscore the importance of home telemonitoring. The conditions examined in this review are critical and necessitate close and regular monitoring that may be achieved at a distance using telemonitoring.

Studies in this area reported a good level of reliability and validity of measures across all pulmonary conditions. The use of advanced technologies may assist in quality control through automatic capturing of lung function data from instrumentation, without patient input regarding the measures. Several studies

 reported the use of telemonitoring systems and technologies that assist in quality control of measurements taken at home and in identification of incorrectly performed pulmonary function tests.

Behavioral effects were discussed in most studies, and patients showed a positive attitude toward telemonitoring and home telecare technologies. However, decreased adherence over time

 necessitates further investigation to identify the reasons for this and to develop appropriate strategies to address it. Several barriers to home spirometry adherence have been identified in the literature, including poor health status, time conflicts, device problems, and lack of ability to operate the system.

 In the case of home telemonitoring, it is important to consider the patient’s condition, the type of telemonitoring service and duration, and the frequency, type, and amount of transferred data. With advancements in technology, home telemonitoring has become an effective and reliable approach that is well accepted by patients and supports patient care at home. It is now time to direct our efforts toward further developing the technology to meet the other needs of patients (eg, user-friendly interfaces, prompts and reminders, and educational material). In fact, integration of reminders and alarms into monitoring devices might improve patient compliance,

 and development of user-friendly interfaces that can be used by patients of all ages is essential to ensure good use of these systems.

At the clinical level, positive effects of home telemonitoring on patients’ medical conditions were identified in this review (eg, detection of complications, better disease control, immediate feedback, and adequate medication use). Never-theless, these results should be interpreted with caution. Unlike home telemonitoring studies on diabetes mellitus and cardiac diseases, several investigations described herein did not conduct a systematic and quantitative evaluation of the clinical effects of telemonitoring or include a randomization or control group.

 Furthermore, most studies herein involving patients with respiratory conditions were limited to small samples and to short study durations, which limits generalizability of the findings and confirmation of these effects.

Assessment of the effects of telemonitoring on the use of health services was performed in few studies. There is no solid evidence about effects of home telemonitoring on the utilization of healthcare resources, and no in-depth analyses were conducted in most instances. In addition, no comparison was performed to examine differences in the amount of time spent by healthcare professionals in the short term in telemonitoring settings vs the amount of time that would be otherwise involved when complications and deteriorations occur in the conditions of their patients. These areas should be studied in the future.

 no detailed cost analysis was performed to estimate the actual savings associated with the use of this approach. To advocate the use of home telemonitoring as a patient management approach and to incorporate it into practice, it is critical to have evidence indicating its economic viability and benefits.

Studies in the area of home telemonitoring for respiratory conditions have demonstrated good quality of transferred data and positive effects on patient behavior, which is consistent with results of telemonitoring investigations in other chronic diseases (diabetes, hypertension, and cardiac diseases).

 Although the effects on the conditions of patients are promising, they cannot be confirmed given limitations in study designs and the analyses conducted. Furthermore, effects on the utilization of health services have not been well assessed, in contrast to other studies of home telemonitoring for diabetes and cardiac diseases.

 Based on existing evidence in the literature, the technology necessary to support patient care at home has matured over time. It is expected that continuous increases in the availability of high-speed Internet access in patients’ homes will further facilitate the use of home telemonitoring for patient care management. Nevertheless, an important issue that remains is how to optimize the use of this technology and how to apply it in the most efficient manner in the clinical context. To do so, it is important to keep in mind that telemonitoring seems most beneficial and useful among patients who have poor perception of their asthma severity, carry significant symptom burden, require critical care management, or have returned home from an emergency department visit or hospitalization.

 In addition, it is essential to integrate telemonitoring into clinical and home contexts and to make it more appealing to providers and to patients (eg, user friendliness, ease of use, reminders and alarms, and decision support tools).

Future studies involving telemonitoring of patients with pulmonary conditions should incorporate more structure in their approach and should consider the use of RCTs with larger samples and longer observation periods to support formal evaluation of the effects of telemonitoring and to allow better generalizability of their findings. Although authors have emphasized the potential benefits of this approach, challenges remain in relation to licensing and reimbursement. An evaluation of the attitude of providers toward this technique is essential to have a more complete picture regarding its feasibility and acceptance and to understand the potential limitations and challenges to its widespread application in clinical practice.

Telfer School of Management (MJ), University of Ottawa, Ottawa, Ontario, Canada; Canada Research Chair in Information Technology in Health Care (GP), HEC Montreal, Montreal, Quebec, Canada; and Department of Health Administration (CS), University of Montreal, Montreal, Canada.

Support for this study was provided by the Canada Research Chairs Program.

The authors (MJ, GP, CS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (MJ, GP, CS); acquisition of data (GP); analysis and interpretation of data (MJ, GP, CS); drafting of the manuscript (MJ); critical revision of the manuscript for important intellectual content (MJ, GP, CS); obtaining funding (GP); and supervision (GP, CS).

Mirou Jaana, PhD, Telfer School of Management, University of Ottawa, 55 Laurier Ave E, Ottawa, ON K1N 6N5, Canada. E-mail: 

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REVIEW ARTICLE

Home telemonitoring represents a promising approach for the management of respiratory conditions and supports early identification of deteriorations in patient condition and symptoms control.

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