Tapping into the behavioral health expertise of all the participants in an COCOA is one of the many reasons CACAOS were established in the first place. Much like CDC couldn’t exist without the efforts of clinicians’ diagnoses in the past, the success of CACAOS depends on as much participation as possible. In this situation, it would be best to incorporate all the structured data, but leave the clinical notes that are less formal out of the cloud and prevent them from being entered into the larger COCOA database that other locations could access through one of the four types of knowledge-based session support (EMIR, HE, ERR, PER).

These sections of records may contain informal notations regarding factors that would contribute to the mental health of the patient, but also certain information that would have to be filtered such as personal life-altering experiences such as rape, molestation, etc. This type of information is easy to gather and integrate into explicit patient information, but is difficult to filter through and formalize into structured experiential knowledge that would be beneficial to other caregivers or in future encounters.

Decision support systems are based on sources of clinical decision making, and those sources need to be as accurate as possible. Excluding this would alleviate some of the concerns regarding the loss of prestige and reputation for the practice as a whole, and filter the informal implicit knowledge from being delivered to the cloud and being added to the practical knowledge bank for the additional COCOA participants. 2. Comment on the environment of the practice and its readiness for strategic change.

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Who might you want to add to the team that is exploring an COCOA strategy? The team seems ready to explore the COCOA treated, as their alternative scenarios seem logistically sound and made sense for adding value. The third scenario regarding early detection and aggressive management of issues would most likely require a clinical forecasting tool that would assist with predictive modeling and allow for the identification of high risk/high cost patients.

Furthermore, adding a Health Information Management Director that could assign clinical informatics specialists to the team responsible for developing behavioral health decision support protocols would add to the readiness of the organization for strategic change. The rigorous decision making process that is involved with creating or contributing to the development of a clinical decision support system or a strategy for an COCOA is one that would definitely call for the expertise of those who work with clinical informatics and information management on a daily basis. 3.

In the face of strong evidence, why do you think that managed behavioral health has been slow to develop and has developed as a niche industry by carving out behavioral health and managing it separately? What are the reasons for this, and what alternative strategies might be considered? It is s common knowledge that behavioral health is not treated the same as clinical health in terms of reimbursement from federal funds, procedures for treatment, etc. Behavioral health management is extremely important because there are so many deaths per year caused by suicides as a result of behavioral health issues such as depression, PETS, etc.

Alcoholics, for example, are not treated the same as patients who have a different chronic disease management requirements such as education for diabetes patients on how to monitor and control their blood sugar, despite alcohol being one of he number one killers in this country, either directly or indirectly. Believe an excellent alternative to the current approach to behavioral health issues would be the incorporation of Telekinetic encounters into the reimbursement schedule for Medicaid patients.

If Medicaid and other payers in the industry would take a more proactive approach to the treatment of diseases and illnesses such as substance abuse or PETS, then there would be more exposure and treatment available to people who suffer from these conditions and it would increase the overall population health in areas where Telekinetic arrives are provided and encounters with psychiatrists can be conducted via videoconferencing between the patients in their home and providers at their usual location that wouldn’t otherwise have been conducted at all. CASE STUDIES CHAPTER 9 1.

Present arguments for including patients and families in the initial team discussions. Including patients and families in the initial team discussion would provide a better perspective for the patient as to what topics are discussed and what efforts go into the multidisciplinary meetings concerning their specific disease. It would allow them an opportunity so see just how much detail is involved with these collaborations, and expose them to the science behind all the discipline-based evidence that is considered when determining best practices for their condition. . What does “confidence” I this case mean? Is it based on intuition, collective judgment (experience), or statistical analysis? Scientific evidence can be provided to justify the use of this information (age, commemorative, and length of time). Predictive modeling software has come a long way since this book was published. Medical artificial intelligence includes complex algorithms that are based on not only scientific and clinical research, UT also statistical analysis of indicators over a certain period of time.

These indicators can often shift or change over a period of time, and the software bases its calculations off of a set of clinical data extracted from multiple sources within an organization over a period of time and modeled using discreet event, agent- based, and system dynamics modeling, including information that may seem irrelevant such as the time of year, how often the patient calls in to inquire about the status of a prescription refill, or has a question about their treatment plan, etc. But actually has an impact on the results of the forecast. How can information on high-risk patients be brought to the point of clinical decision making to guide physicians? More education efforts can be utilized to directly target these high risk/high cost patients. There could be additional efforts such as assigning private duty nurses to tend to the specific needs of these patients. Additional resources such as links to a patient portal, an educational series of videos or presentations could be delivered to the patient or integrated into the managed care plan.

This would impact awareness regarding the illness, and ultimately benefit providers at the point of care. . Is there sufficient data in Centrals ERR and in a form to be mined to build predictive models for identifying high risk patients for all physicians? Individual physicians? What questions regarding the structure of data in the ERR would you ask? It has the potential to benefit all the physicians, because even if they all can’t benefit immediately, the demographic and clinical information in the care management portion of the ERR could be used to assist with future forecasts once they are able to expand.

This is why it’s extremely important to capture as much data as possible and structure t in a format that can be utilized in the future. Each of the variables listed in the text can be beneficial, as the algorithms used to calculate these predictions take all of this information into consideration. I would request that the structure be based on industry standard and that it be stored in a data warehouse that is easily accessible for future applications in order to be interoperable with additional components of the ERR that would contribute to the population management of a larger group of patients in the future. . What might Centrals committee propose given the potential conflict between the mission of the COCOA o improve the health of the patients and the fee-for-service plans of Health First to reimburse based on utilization? They could use a phased approach and request a pilot-program that would allow for alternative reimbursement schedules for certain procedure groups such as Type 2 Diabetes, and learn from the results of that pilot study in order to justify expanding the efforts to other high risk populations. . How can the knowledge derived from population data sets inform clinical decision making by individual physicians? How can these data be accessed by clinics or physicians at the point of care? Providers are going to practice however they would like, but the information provided by this software can aid their decision making process by offering scientific and statistic based suggestions. These suggestions can be in the form of alerts for consideration.

Also, the life coaches and nurses could provide additional notes/indicators to the ERR through care management software, and surveys and questionnaires could be filled out online that would provide additional information that would be beneficial to the physicians that they would not otherwise have. 7. What are he different perspectives on outcomes as defined by Central and Health First? Does predicting areas of decreased resource consumption equate with potential areas of quality improvement? The different perspectives can be demonstrated through the entities’ mission statements.

Health First is focused on finance and profit, while Central is concerned with quality. Predicting areas of decreased resource consumption could decrease cost which would adhere to the mission of Health First. It would identify areas where consumption could be decreased, and depending on what variables are included in predicting the decrease in resource institution it could also affect the quality of care potentially in either a positive or a negative way. This all depends on how these areas are identified.

If it means re-using aprons, and there would be a cost-effective way to clean and sanitize existing ones, then yes it would equate with potential areas of quality improvement because it would have a positive impact on the bottom line without hindering the clinical services provided. 8. Are insurance claims data valid measures of clinical diagnosis and treatment? Depending on how accurate they are, yes. However, there are often times fraudulent or simply erroneous claims lied, so this is something that would have to be carefully considered if to be used in calculations for clinical diagnosis and treatment. . Can the predictive model generalize to patients in other diagnostic categories? The patient population of Central is demographically diverse, so yes it could generalize to patients in other diagnostic categories. Say, for instance, technical and clinical factors included in calculating the best practices for patients who are overweight could also be applied to patients with Diabetes in terms of diet, exercise, and lifestyle changes necessary to improve conditions.


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