STUDY DESIGN

The Documenting Hope Project believes that chronic health issues can only be understood and must be studied from a multifactorial and multidimensional perspective. This approach, unlike the classic “gold standard” clinical study, takes into account the truly complex nature of chronic illness. A strong gene-environment interaction is also increasingly accepted to be at the root of most chronic illnesses. Studies show that environmental factors, such as lifestyle choices, behaviors, exposures, beliefs, etc., are of greater significance than genetics with regard to health outcomes.

Chronic illness study designs to date have largely lacked the capacity to fully elucidate etiology because they are too narrow in scope and breadth. The Documenting Hope Project intends to develop a more robust picture of the multifactorial nature of health using a new study design. Asking flat linear questions about complex multivariable conditions will yield little new knowledge about the condition. Complex conditions require complex study design.

Andersson P. [Increasing documentation for anthroposophic medicine]. Lakartidningen. 2006;103(10):775-6; discussion 7.

Bisgaard H, Vissing NH, Carson CG, Bischoff AL, Folsgaard NV, Kreiner-Moller E, et al. Deep phenotyping of the unselected COPSAC2010 birth cohort study. Clin Exp Allergy. 2013;43(12):1384-94.

Bland DJS. Disease Delusion. S.l.: Harpercollins; 2015.

Bland J. The disease delusion conquering the causes of chronic illness for a healthier, longer, and happier life. New York, NY: HarperWave,; 2014.

Bradley R, Oberg EB, Calabrese C, Standish LJ. Algorithm for complementary and alternative medicine practice and research in type 2 diabetes. J Altern Complement Med. 2007;13(1):159-75.

Buehning LJ, Hedayat KM, Sachdeva A, Golshan S, Lapraz JC. A novel use of biomarkers in the modeling of cancer activity based on the theory of endobiogeny. Glob Adv Health Med. 2014;3(4):55-60.

Burkhard B. [Anthroposophic treatment in pediatrics–a critical analysis]. Versicherungsmedizin. 2004;56(4):197-9.

D’Auria E, Miraglia Del Giudice M, Barberi S, Mandelli M, Verduci E, Leonardi S, et al. Omega-3 fatty acids and asthma in children. Allergy Asthma Proc. 2014;35(3):233-40.

Edelman D, Oddone EZ, Liebowitz RS, Yancy WS Jr, Olsen MK, Jeffreys AS, et al. A multidimensional integrative medicine intervention to improve cardiovascular risk. J Gen Intern Med. 2006 Jul;21(7):728–34.

Hamre HJ, Witt CM, Kienle GS, Glockmann A, Willich SN, Kiene H. Predictors of outcome after 6 and 12 months following anthroposophic therapy for adult outpatients with chronic disease: a secondary analysis from a prospective observational study. BMC Res Notes. 2010;3:218.

Hamre HJ, Kiene H, Ziegler R, Troger W, Meinecke C, Schnurer C, et al. Overview of the Publications From the Anthroposophic Medicine Outcomes Study (AMOS): A Whole System Evaluation Study. Glob Adv Health Med. 2014;3(1):54-70.

Hamre HJ, Kiene H, Kienle GS. Clinical research in anthroposophic medicine. Altern Ther Health Med. 2009;15(6):52-5.

Herbert MR, Weintraub K. The autism revolution: whole-body strategies for making life all it can be. New York: Ballantine Books; 2012. xii, 302 p. P.

Herbert MR, Russo JP, Yang S, Roohi J, Blaxill M, Kahler SG, et al. Autism and environmental genomics. Neurotoxicology. 2006;27(5):671-84.

Investigators M-EN. The MAL-ED study: a multinational and multidisciplinary approach to understand the relationship between enteric pathogens, malnutrition, gut physiology, physical growth, cognitive development, and immune responses in infants and children up to 2 years of age in resource-poor environments. Clin Infect Dis. 2014;59 Suppl 4:S193- 206.

Lambert B, Kobliner V. A compromised generation : the epidemic of chronic illness in America’s children [Internet]. 1st Sentient Publications. Boulder, CO: Sentient Publications; 2010. xiv, 358 p.

Lapraz JC, Hedayat KM. Endobiogeny: a global approach to systems biology (part 1 of 2). Glob Adv Health Med. 2013;2(1):64-78.

Lemer PS, Optometric Extension Program Foundation. Envisioning a bright future: interventions that work for children and adults with autism spectrum disorders. Santa Ana, CA: Optometric Extension Program Foundation; 2008. xvi, 414 p. p.

Rutter M. Changing concepts and findings on autism. J Autism Dev Disord. 2013;43(8):1749-57.

Shmool JL, Kubzansky LD, Newman OD, Spengler J, Shepard P, Clougherty JE. Social stressors and air pollution across New York City communities: a spatial approach for assessing correlations among multiple exposures. Environ Health. 2014;13:91.

Shmool JL, Michanowicz DR, Cambal L, Tunno B, Howell J, Gillooly S, et al. Saturation sampling for spatial variation in multiple air pollutants across an inversion-prone metropolitan area of complex terrain. Environ Health. 2014;13(1):28.

Thompson L, Kemp J, Wilson P, Pritchett R, Minnis H, Toms-Whittle L, et al. What have birth cohort studies asked about genetic, pre- and perinatal exposures and child and adolescent onset mental health outcomes? A systematic review. Eur Child Adolesc Psychiatry. 2010;19(1):1-15.

Wlasiuk G, Vercelli D. The farm effect, or: when, what and how a farming environment protects from asthma and allergic disease. Curr Opin Allergy Clin Immunol. 2012;12(5):461-6.

 

METHODOLOGY: THE IMPERATIVE FOR NEW STUDY DESIGN TO ACCOUNT FOR THE MULTIFACTORIAL NATURE OF CHRONIC ILLNESS ETIOLOGY AND REMISSION

In contrast to a traditionally designed clinical study that studies one molecule (or a single intervention) at a time, an observational assessment allows scientists to observe a multiplicity of variables in a given study environment. Instead of analyzing the impact of one variable (e.g. assessing the impact of one drug on a group of people), an observational assessment permits scientists to collect a large volume of seemingly unrelated information that, upon analysis, may give insight into complex medical problems when mined for multi-system changes over time.

Traditional clinical trials are rarely successful in increasing understanding of the root causes of complex systemic disease or complex epidemiological processes. Many scientists now believe that the complexity of modern illness requires a more complex study design, one that leaves room for multiple, simultaneous factors in exploration of causation, and by extension, in prevention and reversal. We believe that solutions to complex illness will continue to elude us until we learn to ask questions differently.

Ashfield-Watt PA, Welch AA, Godward S, Bingham SA. Effect of a pilot community intervention on fruit and vegetable intakes: use of FACET (Five-a-day Community Evaluation Tool). Public Health Nutr. 2007;10(7):671-80.

Carlson JA, Crespo NC, Sallis JF, Patterson RE, Elder JP. Dietary-related and physical activity-related predictors of obesity in children: a 2-year prospective study. Child Obes. 2012;8(2):110-5.

Edelman D, Oddone EZ, Liebowitz RS, Yancy WS Jr, Olsen MK, Jeffreys AS, et al. A multidimensional integrative medicine intervention to improve cardiovascular risk. J Gen Intern Med. 2006 Jul;21(7):728–34.

Emmett P. Assessing diet in longitudinal birth cohort studies. Paediatr Perinat Epidemiol. 2009;23 Suppl 1:154-73.

Garland AF, Accurso EC, Haine-Schlagel R, Brookman-Frazee L, Roesch S, Zhang JJ. Searching for elements of evidence-based practices in children’s usual care and examining their impact. J Clin Child Adolesc Psychol. 2014;43(2):201-15.

Green CJ, Fortin P, Maclure M, Macgregor A, Robinson S. Information system support as a critical success factor for chronic disease management: Necessary but not sufficient. Int J Med Inform. 2006;75(12):818-28.

Hamre HJ, Witt CM, Glockmann A, Ziegler R, Willich SN, Kiene H. Anthroposophic medical therapy in chronic disease: a four-year prospective cohort study. BMC Complement Altern Med. 2007;7:10.

Hamre HJ, Witt CM, Kienle GS, Glockmann A, Willich SN, Kiene H. Predictors of outcome after 6 and 12 months following anthroposophic therapy for adult outpatients with chronic disease: a secondary analysis from a prospective observational study. BMC Res Notes. 2010;3:218.

Hamre HJ, Witt CM, Kienle GS, Meinecke C, Glockmann A, Willich SN, et al. Anthroposophic therapy for children with chronic disease: a two-year prospective cohort study in routine outpatient settings. BMC Pediatr. 2009;9:39.

Hamre HJ, Witt CM, Kienle GS, Schnurer C, Glockmann A, Ziegler R, et al. Anthroposophic therapy for asthma: A two-year prospective cohort study in routine outpatient settings. J Asthma Allergy. 2009;2:111-28.

Helfand M, Oehlke MA, Lieberman DA. Community-based research–a framework forproblem formulation: the case of upper endoscopy for gastroesophageal reflux disease. Med Decis Making. 1997;17(3):315-23.

Herrmann D, Hebestreit A, Ahrens W. [Impact of physical activity and exercise on bone health in the life course : a review]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2012;55(1):35-54.

Investigators M-EN. The MAL-ED study: a multinational and multidisciplinary approach to understand the relationship between enteric pathogens, malnutrition, gut physiology, physical growth, cognitive development, and immune responses in infants and children up to 2 years of age in resource-poor environments. Clin Infect Dis. 2014;59 Suppl 4:S193-206.

Julvez J, Smith GD, Golding J, Ring S, Pourcain BS, Gonzalez JR, et al. Prenatal methylmercury exposure and genetic predisposition to cognitive deficit at age 8 years. Epidemiology. 2013;24(5):643-50.

Linneman C, Hessler K, Nanney S, Steger-May K, Huynh A, Haire-Joshu D. Parents are accurate reporters of their preschoolers’ fruit and vegetable consumption under limited conditions. J Nutr Educ Behav. 2004;36(6):305-8.

O’Brien M, Nader PR, Houts RM, Bradley R, Friedman SL, Belsky J, et al. The ecology of childhood overweight: a 12-year longitudinal analysis. Int J Obes (Lond). 2007;31(9):1469-78.

Watt T, Hegedus L, Bjorner JB, Groenvold M, Bonnema SJ, Rasmussen AK, et al. Is Thyroid Autoimmunity per se a Determinant of Quality of Life in Patients with Autoimmune Hypothyroidism? Eur Thyroid J. 2012;1(3):186-92.

 

INDIVIDUALIZED, COLLABORATIVE, INTEGRATIVE, AND PARTICIPATORY CARE

The Documenting Hope Project offers a uniquely comprehensive and personalized healing program whose hallmarks are collaborative interdisciplinary teams of clinicians and health professionals, the use of bio-individual assessments, personalized therapeutic care and support (including personalized nutrition) and home and environs modification. The program emphasizes the importance of nutrition and lifestyle, and requires full engagement from participants and their families. A strong foundation of literature demonstrates how this methodology can positively impact health outcomes, even though combining these components is uncommon in most clinical settings.

Abman S, Jobe A, Chernick V, Blaisdell C, Castro M, Ramirez MI, et al. Strategic plan for pediatric respiratory diseases research: an NHLBI working group report. Pediatr Pulmonol. 2009;44(1):2-13.

Arman M, Ranheim A, Rehnsfeldt A, Wode K. Anthroposophic health care–different and home-like. Scand J Caring Sci. 2008;22(3):357-66.

Arnold D, Jones BL. Personalized medicine: a pediatric perspective. Curr Allergy Asthma Rep. 2009;9(6):426-32.

Bland J. Alternative therapies–a moving target. Altern Ther Health Med. 2005;11(2):20-2.

Bland JS. Psychoneuro-nutritional medicine: an advancing paradigm. Altern Ther Health Med. 1995;1(2):22-7.

Hamre HJ, Witt CM, Glockmann A, Ziegler R, Willich SN, Kiene H. Anthroposophic medical therapy in chronic disease: a four-year prospective cohort study. BMC Complement Altern Med. 2007;7:10.

Hamre HJ, Witt CM, Kienle GS, Glockmann A, Willich SN, Kiene H. Predictors of outcome after 6 and 12 months following anthroposophic therapy for adult outpatients with chronic disease: a secondary analysis from a prospective observational study. BMC Res Notes. 2010;3:218.

Hamre HJ, Kiene H, Ziegler R, Troger W, Meinecke C, Schnurer C, et al. Overview of the Publications From the Anthroposophic Medicine Outcomes Study (AMOS): A Whole System Evaluation Study. Glob Adv Health Med. 2014;3(1):54-70.

Hamre HJ, Witt CM, Kienle GS, Glockmann A, Ziegler R, Rivoir A, et al. Anthroposophic therapy for migraine: a two-year prospective cohort study in routine outpatient settings. Open Neurol J. 2010;4:100-10.

McCloud E, Papoutsakis C. A medical nutrition therapy primer for childhood asthma: current and emerging perspectives. J Am Diet Assoc. 2011 Jul;111(7):1052–64.

Minich DM, Bland JS. Personalized lifestyle medicine: relevance for nutrition and lifestyle recommendations. ScientificWorldJournal. 2013;2013:129841.

Naber CM, Water-Schmeder O, Bohrer PS, Matonak K, Bernstein AL, Merchant MA. Interdisciplinary treatment for vestibular dysfunction: the effectiveness of mindfulness, cognitive-behavioral techniques, and vestibular rehabilitation. Otolaryngol Head Neck Surg. 2011;145(1):117-24.

Schiltz B, Minich DM, Lerman RH, Lamb JJ, Tripp ML, Bland JS. A science-based, clinically tested dietary approach for the metabolic syndrome. Metab Syndr Relat Disord. 2009;7(3):187-92.

Sela DA, Mills DA. The marriage of nutrigenomics with the microbiome: the case of infant-associated bifidobacteria and milk. Am J Clin Nutr. 2014;99(3):697S-703S.

Sexson EL, Monaghan MS, Lenz TL, Haddad AR, Jensen G, Elsasser G. Use of a multidisciplinary tool to achieve target outcomes in Native American patients with diabetes: Treat-to-target. J Multidiscip Healthc. 2008;1:73-7.

Wise M, Gustafson DH, Sorkness CA, Molfenter T, Staresinic A, Meis T, et al. Internet telehealth for pediatric asthma case management: integrating computerized and case manager features for tailoring a Web-based asthma education program. Health Promot Pract. 2007;8(3):282-91.

 

COMMUNITY-BASED SCIENCE

Ashfield-Watt PA, Welch AA, Godward S, Bingham SA. Effect of a pilot community intervention on fruit and vegetable intakes: use of FACET (Five-a-day Community Evaluation Tool). Public Health Nutr. 2007;10(7):671-80.

Carlson JA, Crespo NC, Sallis JF, Patterson RE, Elder JP. Dietary-related and physical activity-related predictors of obesity in children: a 2-year prospective study. Child Obes. 2012;8(2):110-5.

Emmett P. Assessing diet in longitudinal birth cohort studies. Paediatr Perinat Epidemiol. 2009;23 Suppl 1:154-73.

Garland AF, Accurso EC, Haine-Schlagel R, Brookman-Frazee L, Roesch S, Zhang JJ. Searching for elements of evidence-based practices in children’s usual care and examining their impact. J Clin Child Adolesc Psychol. 2014;43(2):201-15.

Green CJ, Fortin P, Maclure M, Macgregor A, Robinson S. Information system support as a critical success factor for chronic disease management: Necessary but not sufficient. Int J Med Inform. 2006;75(12):818-28.

Hamre HJ, Witt CM, Glockmann A, Ziegler R, Willich SN, Kiene H. Anthroposophic medical therapy in chronic disease: a four-year prospective cohort study. BMC Complement Altern Med. 2007;7:10.

Hamre HJ, Witt CM, Kienle GS, Glockmann A, Willich SN, Kiene H. Predictors of outcome after 6 and 12 months following anthroposophic therapy for adult outpatients with chronic disease: a secondary analysis from a prospective observational study. BMC Res Notes. 2010;3:218.

Hamre HJ, Witt CM, Kienle GS, Meinecke C, Glockmann A, Willich SN, et al. Anthroposophic therapy for children with chronic disease: a two-year prospective cohort study in routine outpatient settings. BMC Pediatr. 2009;9:39.

Hamre HJ, Witt CM, Kienle GS, Schnurer C, Glockmann A, Ziegler R, et al. Anthroposophic therapy for asthma: A two-year prospective cohort study in routine outpatient settings. J Asthma Allergy. 2009;2:111-28.

Helfand M, Oehlke MA, Lieberman DA. Community-based research–a framework for problem formulation: the case of upper endoscopy for gastroesophageal reflux disease. Med Decis Making. 1997;17(3):315-23.

Herrmann D, Hebestreit A, Ahrens W. [Impact of physical activity and exercise on bone health in the life course : a review]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2012;55(1):35-54.

Investigators M-EN. The MAL-ED study: a multinational and multidisciplinary approach to understand the relationship between enteric pathogens, malnutrition, gut physiology, physical growth, cognitive development, and immune responses in infants and children up to 2 years of age in resource-poor environments. Clin Infect Dis. 2014;59 Suppl 4:S193- 206.

Julvez J, Smith GD, Golding J, Ring S, Pourcain BS, Gonzalez JR, et al. Prenatal methylmercury exposure and genetic predisposition to cognitive deficit at age 8 years. Epidemiology. 2013;24(5):643-50.

Linneman C, Hessler K, Nanney S, Steger-May K, Huynh A, Haire-Joshu D. Parents are accurate reporters of their preschoolers’ fruit and vegetable consumption under limited conditions. J Nutr Educ Behav. 2004;36(6):305-8.

O’Brien M, Nader PR, Houts RM, Bradley R, Friedman SL, Belsky J, et al. The ecology of childhood overweight: a 12-year longitudinal analysis. Int J Obes (Lond). 2007;31(9):1469-78.

Watt T, Hegedus L, Bjorner JB, Groenvold M, Bonnema SJ, Rasmussen AK, et al. Is Thyroid Autoimmunity per se a Determinant of Quality of Life in Patients with Autoimmune Hypothyroidism? Eur Thyroid J. 2012;1(3):186-92.

 

HEALTH COACHING/PERSONALIZED HEALTH MANAGEMENT

Health care practitioners confirm that their patients with complex chronic illnesses requiring substantial dietary and/or lifestyle changes have improved health outcomes when supported by a trained health coach or wellness consultant. While much of this research involves an aging population, evidence supports this approach with children.

Burti L, Amaddeo F, Ambrosi M, Bonetto C, Cristofalo D, Ruggeri M, et al. Does additional care provided by a consumer self-help group improve psychiatric outcome? A study in an Italian community-based psychiatric service. Community Ment Health J. 2005;41(6):705-20.

Hermens H, op den Akker H, Tabak M, Wijsman J, Vollenbroek M. Personalized Coaching Systems to support healthy behavior in people with chronic conditions. J Electromyogr Kinesiol. 2014;24(6):815-26.

Karagiozoglou-Lampoudi T, Daskalou E, Agakidis C, Savvidou A, Apostolou A, Vlahavas G. Personalized diet management can optimize compliance to a high-fiber, high-water diet in children with refractory functional constipation. J Acad Nutr Diet. 2012;112(5):725-9.

Kogut SJ, Johnson S, Higgins T, Quilliam B. Evaluation of a program to improve diabetes care through intensified care management activities and diabetes medication copayment reduction. J Manag Care Pharm. 2012;18(4):297-310.

Phillips S, Nonzee N, Tom L, Murphy K, Hajjar N, Bularzik C, et al. Patient navigators’ reflections on the navigator-patient relationship. J Cancer Educ. 2014;29(2):337-44.

Ruiz-Robledillo N, Moya-Albiol L. Self-reported health and cortisol awakening response in parents of people with asperger syndrome: the role of trait anger and anxiety, coping and burden. Psychol Health. 2013;28(11):1246-64.

Smith LL, Lake NH, Simmons LA, Perlman A, Wroth S, Wolever RQ. Integrative Health Coach Training: A Model for Shifting the Paradigm Toward Patient-centricity and Meeting New National Prevention Goals. Glob Adv Health Med. 2013;2(3):66-74.

 

ELECTRONIC HEALTH RECORDS, BIOINFORMATICS AND DATA CAPTURE

The Electronic Health Record and Bioinformatics System utilized by the Documenting Hope Project will serve several key functions:

  • Track and monitor the cases of children enrolled in the recovery program.
  • Store complex sets of data on each program participant including, but not limited to: clinical laboratory data, daily logging (sleep, diet, exercise, emotions, behaviors, etc.), experts assessments (e.g. structural or functional assessments)
  • Act as a communication tool and platform for clinicians and experts to use while collaborating and conferring on cases.
  • Capture survey data from thousands of applicants on environmental exposures and health history.
  • Facilitate the mining and analytics of collected data both during and after the completion of the program.

Similar programs and bioinformatics systems are currently in use to track longitudinal data, assess relative risk, severity of illness, multivariate interventions and health outcomes. A robust cloud-based informatics system that can upload electronic data from labs, consumer apps, and clinician desktops is a central feature of the Documenting Hope Project. This type of platform is readily available, customizable and has been demonstrated to be an efficient way to track “big data” that can then be analyzed, interpreted, and contribute to great advances in the understanding of human health and physiology.

Green CJ, Fortin P, Maclure M, Macgregor A, Robinson S. Information system support as a critical success factor for chronic disease management: Necessary but not sufficient. Int J Med Inform. 2006;75(12):818-28.

Baldwin G. Tracking population health. Health Data Manag. 2013;21(8):22-6.

Batal I, Valizadegan H, Cooper GF, Hauskrecht M. A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data. ACM Trans Intell Syst Technol. 2013;4(4).

Buchwald D, Herrell R, Ashton S, Belcourt M, Schmaling K, Goldberg J. The Chronic Fatigue Twin Registry: method of construction, composition, and zygosity assignment. Twin Res. 1999;2(3):203-11.

Bunyavanich S, Schadt EE. Systems biology of asthma and allergic diseases: a multiscale approach. J Allergy Clin Immunol. 2015;135(1):31-42.

Callier SL, Schmidt H. Managing patient expectations about deidentification. Am J Bioeth. 2010;10(9):21-3.

Desai JR, Wu P, Nichols GA, Lieu TA, O’Connor PJ. Diabetes and asthma case identification, validation, and representativeness when using electronic health data to construct registries for comparative effectiveness and epidemiologic research. Med Care. 2012;50 Suppl:S30-5.

Dutton RP. Quality management and registries. Anesthesiol Clin. 2014;32(2):577-86.

Fernandes AC, Cloete D, Broadbent MT, Hayes RD, Chang CK, Jackson RG, et al. Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records. BMC Med Inform Decis Mak. 2013;13:71.

FitzHenry F, Murff HJ, Matheny ME, Gentry N, Fielstein EM, Brown SH, et al. Exploring the frontier of electronic health record surveillance: the case of postoperative complications. Med Care. 2013;51(6):509-16.

Frey LJ, Sward KA, Newth CJ, Khemani RG, Cryer ME, Thelen JL, et al. Virtualization of Open-Source Secure Web Services to Support Data Exchange in a Pediatric Critical Care Research Network. J Am Med Inform Assoc. 2015.

Gardner J, Xiong L, Xiao Y, Gao J, Post AR, Jiang X, et al. SHARE: system design and case studies for statistical health information release. J Am Med Inform Assoc. 2013;20(1):109-16.

Geyer J, Myers K, Vander Stoep A, McCarty C, Palmer N, DeSalvo A. Implementing a lowcost web-based clinical trial management system for community studies: a case study. Clin Trials. 2011;8(5):634-44.

Hanauer DA, Miela G, Chinnaiyan AM, Chang AE, Blayney DW. The registry case finding engine: an automated tool to identify cancer cases from unstructured, free-text pathology reports and clinical notes. J Am Coll Surg. 2007;205(5):690-7.

Harris ES, Erickson SD, Tolopko AN, Cao S, Craycroft JA, Scholten R, et al. Traditional Medicine Collection Tracking System (TM-CTS): a database for ethnobotanically driven drug-discovery programs. J Ethnopharmacol. 2011;135(2):590-3.

Hu Z, Jin B, Shin AY, Zhu C, Zhao Y, Hao S, et al. Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study. Interact J Med Res. 2015;4(1):e2.

Rosen RC, Marx BP, Maserejian NN, Holowka DW, Gates MA, Sleeper LA, et al. Project VALOR: design and methods of a longitudinal registry of post-traumatic stress disorder (PTSD) in combat-exposed veterans in the Afghanistan and Iraqi military theaters of operations. Int J Methods Psychiatr Res. 2012;21(1):5-16.

Schad PA, Mobley LR, Hamilton CM. Building a biomedical cyberinfrastructure for collaborative research. Am J Prev Med. 2011;40(5 Suppl 2):S144-50.

Shaikh AR, Butte AJ, Schully SD, Dalton WS, Khoury MJ, Hesse BW. Collaborative biomedicine in the age of big data: the case of cancer. J Med Internet Res. 2014;16(4):e101.

Shirts BH, Jackson BR, Baird GS, Baron JM, Clements B, Grisson R, et al. Clinical laboratory analytics: Challenges and promise for an emerging discipline. J Pathol Inform. 2015;6:9.

Sujansky W, Chang S. The California Clinical Data Project: a case study in the adoption of clinical data standards for quality improvement. J Healthc Inf Manag. 2006;20(3):71-8.

Suh KS, Sarojini S, Youssif M, Nalley K, Milinovikj N, Elloumi F, et al. Tissue banking, bioinformatics, and electronic medical records: the front-end requirements for personalized medicine. J Oncol. 2013;2013:368751.