Disease management is the concept of reducing health care costs and Disease management programs are designed to improve the health of persons with.
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In this case, required inputs are usually temperature, humidity, precipitation, wind and net radiation e. An approach that utilizes these variables and is based on the Penman-Monteith method of estimating evapotranspiration was shown to give good results in maize, grape, coffee, soybean and tomato crops in Brazil and Canada Figure 3 , Sentelhas et al.
Obtaining good radiation data is often the biggest challenge since solar radiation observations are not nearly as common as temperature, humidity, precipitation and wind measurements; and longwave sky radiation is seldom measured so it must be estimated from cloud, humidity and temperature data. If we are willing to accept lower estimation accuracy in some cases, and use an LWD estimation scheme that may require local calibration before using it in different locations, then the input demands can be relaxed. For example, hours with RH above some threshold value may be a satisfactory estimator of LWD provided that appropriate thresholds are determined using local observations Figure 4 , Sentelhas et al.
The synergy between agrometeorology and plant pathology has yielded a number of schemes for more effective management of economically important plant diseases, and some of these schemes have also been used to evaluate the climatic risk for plant disease in studies about crop zoning. There are many crops where genetic resistance to disease is not sufficiently high, so chemical controls are necessary to preserve marketable yields.
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Suitable weather-based advice can allow optimal timing of these disease control measures. When spray applications timed by the calendar at regular fixed intervals are replaced by weather-timed applications, fewer sprays are usually needed and a " triple win" results. The grower wins twice by saving both money and time, and the environment wins because just the right number of sprays is applied. Another example was presented by Pedro Junior et al.
Schemes are also being developed to use LWD data in the management of grape diseases Figure 5. For practical application, the most successful management schemes that have grown out of collaboration between agrometeorologists and plant pathologists are for situations where the plants can tolerate some moderate level of disease.
In these cases, observations of temperature and moisture can be used to assign a disease severity value DSV rating to each day's weather, and these ratings can be accumulated to a threshold that triggers disease control action.
Disease management (health) - Wikipedia
An example is shown in Table 2 for the TomCast disease management scheme used in tomatoes Gillespie et al. The first fungicide spray is applied after the DSV sum reaches 35, and additional sprays are applied each time 20 more DSV have accumulated. The key advantage here is that observed data can be used; no forecast is required. For situations where a single weather event can trigger economically significant disease damage e.
It is still a difficult challenge for agrometeorologists to provide sufficiently accurate forecasts of disease-prone weather, especially when the source of moisture is scattered convective rain showers. What future developments can be seen that will enhance the contributions of agrometeorology to plant disease management? At the present time, the delivery of weather-based disease management advice is confined to regions where a suitable set of weather stations can be deployed, and the data can be retrieved sufficiently quickly by phone or radio links.
Such networks are not available in many locations where weather-based disease advice could be very useful. But high-resolution computer weather models are now providing all the hourly meteorological inputs required for the estimation of plant disease potential temperature, humidity, precipitation, wind, and radiation at grid spacing as small as 1 km. Their output within 12 hours of their initialization time is becoming accurate enough to compete with direct observations. Disease management schemes could therefore be run for areas not served by a ground-based network of measurements by utilizing this computer output twice daily.
The problem of correctly locating the occurrence of scattered convective rainfall, as mentioned above, would still exist.
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However, the use of weather radar could come to the rescue here. A study utilizing weather radar in Canada showed that this technology was sufficiently good to provide the necessary information on rainfall durations to successfully run the TomCast disease management scheme for tomatoes. During the tomato-growing season at Elora in southern Ontario, Canada, there were 18 days when leaf wetness was caused by rainfall.
Weather radar was used to determine the leaf wetness duration during these wet periods. The accumulated DSV total estimated from radar over these periods was only 2 DSV different from the value measured by leaf wetness sensors at the site. This is well below the error of 20 DSV required to initiate an unwanted spray Rowlandson, We suggest that the time is ripe for in-depth evaluation of short term output from top quality computer weather models, combined with weather radar data, as potential tools to greatly enhance the spatial availability of weather-based disease management schemes.
Further in the future, as numerical weather models continue to improve, our ability to give forecasted warnings of impending disease-prone weather will be enhanced. This will be particularly helpful for those cases where growers must be prepared in advance to battle a single disease-favorable weather event, as discussed earlier.
One of its concerns was that some payors had been trying to attract only young, healthy patients, which left the other payors at an economic disadvantage. To discourage this type of cherry picking, Germany began giving its public payors extra funding for patients with chronic conditions.
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However, the size of the extra payments was capped to prevent health care costs from rising uncontrollably. Germany also coupled the increased funding with a requirement that the payors enroll patients with the most common chronic conditions in DMPs. Last year, Germany added congestive heart failure to this list. To overcome some of the problems that had hampered earlier DMPs, Germany set minimum standards for them. In addition, the programs had to be approved by a federal health agency and then run nationwide.
Although the program is only six years old, it has already enrolled more than three million patients Exhibit 3 and has demonstrated that it markedly improves health care delivery to those patients. For example, the patients are now significantly more likely to have their feet checked regularly by a specialist, as a result of which the incidence of certain types of foot ulcer has plummeted. Preliminary evidence also suggests that the program may be decreasing mortality. Antje Miksch et al.
Furthermore, patient satisfaction with treatment has risen markedly, and the overall cost of care has decreased; the small increases the program has produced in outpatient and pharmaceutical costs have been more than offset by a drop of more than 25 percent in inpatient costs Exhibit 4. Most of these costs result from additional fees paid to doctors. However, conversations we have had with several payors suggest that many of the programs, especially those for CAD and COPD, are also producing strong results.
Although asthma and COPD are two separate diseases, they share many underlying pathophysiologic mechanisms and are often treated with similar medications. Lotte Steuten et al. It has also reduced total per-patient health care costs. This DMP, which focuses on patients with impaired glucose tolerance who are at high risk of diabetes , has helped the patients lose weight and reduced by two-thirds the risk that they would develop diabetes. Kinori Kosaka et al. Donatella Del Sindaco et al. A Swedish heart-failure DMP has also shortened the duration of hospital stays although it has not lowered the hospitalization rate itself and improved survival rates among enrolled patients.
Anna Stromberg et al. Sweden has also had success using DMPs for patients with hypertension and cancer.
Many other successful programs from other countries are now being reported. We analyzed a wide range of DMPs from countries around the world to determine the characteristics that differentiate successful and unsuccessful programs. Five traits seemed to be the most important in ensuring that DMPs meet their goals: Large DMPs are more likely to be successful than small programs because they benefit from economies of scale: As a result, it is often easier to achieve net savings.
Large programs are also more likely to improve their processes regularly, because they are usually better able to mine the data they are collecting and to use those analyses to refine their processes. Furthermore, large DMPs often find it easier to develop and to ensure compliance with the evidence-based care pathways used for treatment. In small programs, individual physicians may feel that they have retained the right to manage patients according to their preference; the scale of large programs makes it easier to convince physicians to follow the care pathways and to make sure they have the equipment necessary for compliance.
Few general practitioners, for example, have the equipment needed to perform lung function studies in their offices. However, if they are participating in a large DMP that requires them to monitor lung function in their asthma or COPD patients, they will obtain the necessary equipment. Small DMPs may also lack the statistical power to prove that better outcomes were achieved, particularly when the patients enrolled have high health care utilization rates.
Many of the early DMPs had fewer than patients in them; it is perhaps not surprising that their results were often inconclusive or negative. In addition, small DMPs are more likely to be prone to selection bias. Small programs have greater difficulties controlling for these factors, particularly if their eligibility criteria are so strict that only a minority of potential patients can be enrolled.
In contrast, large DMPs with less strict eligibility requirements not only are more likely to achieve statistically significant results but also to produce results that can be generalized to all patients with a given disease. Many early DMPs were able to enroll less than 10 percent of their target populations; in some regions of Germany, participation rates are as high as 90 percent.
Many of the early DMPs were unnecessarily complex—they were not easy to run, their requirements were not easy to follow, and the bureaucratic hurdles they presented to physicians were high. It is perhaps not that surprising, therefore, that many of the programs had difficulty enrolling patients. In contrast, successful DMPs tend to be very simple. For example, they avoid overly restrictive enrollment criteria, and they use uncomplicated care pathways that do not attempt to tailor treatment to multiple small subgroups of patients. In addition, responsibilities are clearly identified in successful DMPs.
One person often, a general practitioner is responsible for all care delivered to a given patient Exhibit 5. That person establishes a care plan with clear-cut goals, evaluates the patient regularly, and monitors how well the patient is adhering to the care plan. He or she is also responsible for coordinating any tests or treatments administered by specialists.
For example, one program we investigated was designed to study the use of a sophisticated telemonitoring device in patients with heart failure. The interventions they include are applicable to the vast majority of enrolled patients, as well as simple and easy to implement. The patients are given ongoing, disease-specific coaching to maximize their ability to care for themselves. Finally, successful DMPs do not assume that patient compliance will be percent at all times. Many early DMPs did not have good mechanisms to prove their effectiveness because they did not have systems in place to monitor what patients were doing and what results were being achieved.
Disease Management
In other cases, data were collected, but there was often a long time lag before the data were processed. In addition, some of the early DMPs did not account fully for their own costs and thus could not determine whether they were actually saving money. In contrast, successful DMPs define the metrics they want to measure about utilization rates, health outcomes both short term and long term , patient satisfaction, and costs before they launch; they also put mechanisms in place to collect the necessary information from the start. They then collect data regularly and analyze it promptly.
Furthermore, the best DMPs use the collected data in two ways. First, the programs themselves evaluate the data routinely to assess their success and to determine whether refinements to their care pathways or other processes are required. Second, they send their data to authoritative third parties for independent analysis, review, and publication of results, because only independent evaluation guarantees that results are bias-free, and only by sharing data will the results be helpful to other programs. Use of the collected data in these two ways also gives patients greater confidence in the quality of care they are receiving, permits providers to benchmark their performance against their peers, and enables researchers to improve evidence-based care recommendations.
In our experience, as long as one stakeholder lacks a strong incentive to move in the same direction as the others, a DMP is unlikely to produce good results. For example, patients can be told that participation in the program can improve their quality of life, decrease the likelihood that their condition will worsen, and make it easier for them to navigate the health system. Where local practices permit, patients can be offered financial incentives, such as a decrease in copayment rates. In a health system with competing payors, participation in a successful DMP can give a payor an image and marketing advantage.
Participation also gives payors better access to patient-specific data and the cost savings that will accrue as the health status of enrolled patients improves.
However, health systems can also offer payors more concrete incentives to participate; as discussed earlier, Germany gives payors a subsidy for each enrolled patient.